Statistics with R and SQL – Page 2

Basics of Probability

In this post am going to introduce into some of the basic principles of probability – and use it in other posts going forward. Quite a number of people would have learned these things in high school math and then forgotten – I personally needed a refresher. These concepts are very useful if we can keep them fresh in mind while dealing with more advanced programming in R and data analysis.
Probability is an important statistical and mathematical concept to understand. In simple terms – probability refers to the chances of possible outcome of an event occurring within the domain of multiple outcomes. Probability is indicated by a whole number – with 0 meaning that the outcome has no chance of occurring and 1 meaning that the outcome is certain to happen. So it is mathematically represented as P(event) = (# of outcomes in event / total # of outcomes). In addition to understanding this simple thing, we will also look at a basic example of conditional probability and independent events.

Let us take an example to study this more in detail.
I am going to use the same dataset that I used for normal distribution – life expectancy from WHO. My dataset has 5 fields – Country, Year, Gender, Age. Am going to answer some basic questions on probability with this dataset.

1 What is the probability that a random person selected is female? It is the total # of female persons divided by all persons, which gives us 0.6. (am not putting out the code as this is just very basic stuff). Conversely, the ‘complement rule’ as it is called means the probabilty of someone being male is 0.4. This can be computed as 1 – 0.6 or total # of men /total in sample. Since being both male and female is not possible, these two events can be considered mutually exclusive.

2 Let us look at some conditional probability. What is the probability that a female is selected given that you only have people over 50? The formula for this is probability of selecting a female over 50 divided by probability of selecting someone over 50.

DECLARE @COUNT NUMERIC(8, 2), @COUNTFEMALE NUMERIC(8, 2),@COUNTMALE NUMERIC(8, 2)
SELECT @COUNTFEMALE = COUNT(*) FROM [dbo].[WHO_LifeExpectancy] WHERE GENDER = 'female' and AGE > 50
SELECT @COUNT = COUNT(*) FROM [dbo].[WHO_LifeExpectancy]
SELECT 'PROBABILITY OF A FEMALE OVER 80' , @COUNTFEMALE/@COUNT
SELECT @COUNTMALE = COUNT(*) FROM [dbo].[WHO_LifeExpectancy] WHERE age > 50
SELECT @COUNT = COUNT(*) FROM [dbo].[WHO_LifeExpectancy]
SELECT 'PROBABILITY OF ANY RANDOM PERSON over 50' , @COUNTMALE/@COUNT
SELECT 'PROBABILITY OF SELECTING A FEMALE FROM OVER 50', @COUNTFEMALE/@COUNTMALE

PROBABILITY1

3 Do we know if these two events are dependent or independent of each other? That is , is does selecting a female over 50 affect the probability of selecting any person over 50? To find this out we have to apply the formula for independence – which is that the probability of their intersection should equal the product of their unconditional probabilities..that is probability of female over 50 multiplied by probability of female should equal probability of female and 50.

DECLARE @pro50andfemale numeric(12, 2), @total numeric(12, 2), @female numeric(12, 2), @fifty numeric(12, 2)
select @pro50andfemale= count(*) from [dbo].[WHO_LifeExpectancy] where age > 50 and gender = 'female'
select @total = count(*) from [dbo].[WHO_LifeExpectancy]
select 'Probability of 50 and female' ,round(@pro50andfemale/@total, 2)

select @female = count(*) from [dbo].[WHO_LifeExpectancy] where gender = 'female'
select 'Probability of female', @female/@total
select @fifty = count(*) from [dbo].[WHO_LifeExpectancy] where age > 50
select 'Probability of fifty', @fifty/@total
select 'Are these independent ', round((@female/@total), 2)*round((@fifty/@total), 2)

Results are as below..as we can see, the highlighted probability #s are very close suggesting that these two events are largely independent.

PROBABILITY1

From the next post am going to look into some of these concepts with R and do some real data analysis with them. Thanks for reading.

Generating Frequency Table

This week’s blog post is rather simple. One of the main characteristics of a data set involving classes, or discrete variables – are frequencies. The number of times each data element or class is observed is called its frequency. A table that displays the discrete variable and number of times it occurs in the data set is called a ‘Frequency Table’.A frequency table usually has frequency, percent or relative frequency expressed in % (the percentage of occurrences), cumulative frequency – the number of times all the preceding values have occurred and Cumulative Relative Frequency which is the ratio of cumulative frequency to size of sample (this can also be expressed as a percent if so desired).

For creating this I used the same data set I used for studying Simpson’s Paradox
To remember that variables we are deriving frequency for should be DISCRETE in nature and each instance of the variable should be related/comparable to another in some way. If we are interested in cumulative values – those should make some kind of sense..so just summing all records before a name, or before a product(even though those classify as discrete), may not really make sense in most situations .In this case my variable is the age cohort, so summing whatever is below a certain age cohort can be useful data.

My TSQL for this is as below:

DECLARE @totalcount numeric(18, 2)
SELECT @totalcount = COUNT(*) FROM [dbo].[paradox_data]
;WITH agecte AS 
(
 SELECT [agecohort], c = COUNT(agecohort) 
 FROM [dbo].[paradox_data]
 GROUP BY [AgeCohort] 
)
SELECT agecte.[agecohort], c as frequency,(agecte.c/@totalcount)*100 AS [percent], 
 cumulativefrequency = SUM(c) OVER (ORDER BY [agecohort] ROWS UNBOUNDED PRECEDING),
 cumulativepercent = ((SUM(c) OVER (ORDER BY [agecohort] ROWS UNBOUNDED PRECEDING))/@totalcount)*100
FROM agecte
ORDER BY agecte.[agecohort];

My results are as below. I have 1000 records in the table. This tells me that I have 82 occurences of age cohort 0-5, 8.2% of my dataset is from this bracket, 82 again is the cumulative frequency since this is the first record and 8.2 cumulative percent. For the next bracket 06-12 I have 175 occurences, 17.5 %, 257 occurences of age below 12, and 25.7 % of my data is in this age bracket. And so on.

freq1

Let us try the same thing with R. As it is with most R code, part of this is already written in a neat little function here. I did however, find some issues with this code and had to modify it. The main issue was that the function was calculating ‘length’ of the dataframe (in sql terms – number of records) from the variable – and R kept returning a value of ‘1’. The correct way to calculate number of records is to specify the field name after the dataframe, so when I did that it was able to get to the total number of records. This is a little nuance/trick with R that gets many sql people confused, and was happy to have figured it out. I do not know of a ‘macro’ or a generalised function that can pull this value so I had to stick it to the function. It will be different if you use another field for sure. The modified function and value it returns is as below.

##This is my code to initiate R and bring in the dataset

install.packages("RODBC")
library(RODBC)

cn <- odbcDriverConnect(connection="Driver={SQL Server Native Client 11.0};server=MALATH-PC\\SQL01;database=paradox_data;Uid=sa;Pwd=mypwd")data <- sqlQuery(cn, 'select agecohort from [dbo].[paradox_data]')

make_freq_table <- function( lcl_list )


 {
 ## This function will create a frequency table for 
 ## the one variable sent to it where that 
 ## table gives the items, the frequency, the relative 
 ## frequeny, the cumulative frequency, the relative
 ## cumulative frequency
 ## The actual result of this function is a data frame 
 ## holding that table.
 lcl_freq <- table( lcl_list )
 

##had to change this from original code to pull correct length.

lcl_size <- length( lcl_list$agecohort )
 lcl_df <- data.frame( lcl_freq )
 names( lcl_df ) <- c("Items","Freq")
 lcl_values <- as.numeric( lcl_freq )
 lcl_df$rel_freq_percent <- (lcl_values / lcl_size)*100
 lcl_df$cumul_freq <- cumsum( lcl_values )
 lcl_df$rel_cumul_freq_percent <- (cumsum( lcl_values ) / lcl_size)*100
lcl_df
 } 

make_freq_table(data)

The result I get is as below and is the same as what I got with T-SQL.

freq2

Since this is a rather simple example that is 100 percent do-able in TSQL – I did not find the need to do it by calling R from within it. It did help me learn some nuances about R though.

Thanks for reading.

The Empirical Rule

I am resuming technical blogging after a gap of nearly a month. I will continue to blog my re learning of statistics and basic concepts, and illustrate them to the best of my ability using R and T-SQL where appropriate.

For this week I have chosen a statistical concept called ‘Empirical Rule’.

The empirical rule is a test to determine if or not the dataset belongs to a normal distribution.

68% of data falls within the first standard deviation from the mean.
95% fall within two standard deviations.
99.7% fall within three standard deviations.

The rule is also called the 68-95-99 7 Rule or the Three Sigma Rule. The basic reason to test if a dataset follows this rule is to determine whether or not it is a normal distribution. When it is normally distributed and follows the bell curve – it lends itself easily to a variety of statistical analysis, especially forecasting. There are many other tests for normalisation that are more rigorous than this – and the wiki link has a good summary of those, but this is a basic and easy test.
For my test purpose I used the free dataset from WHO 0n life expectancies around the globe. It can be found here. I imported this dataset into sql server. I ran a simple TSQL script to find out if this dataset satisfies the empirical rule.

DECLARE @sdev numeric(18,2), @mean numeric(18, 2), @sigma1 numeric(18, 2), @sigma2 numeric(18, 2), @sigma3 numeric(18, 2)
DECLARE @totalcount numeric(18, 2)
SELECT @sdev = SQRT(var(age)) FROM [dbo].[WHO_LifeExpectancy]
SELECT @mean = sum(age)/count(*) FROM [dbo].[WHO_LifeExpectancy]
SELECT @totalcount = count(*) FROM [dbo].[WHO_LifeExpectancy]
SELECT @sigma1 = (count(*)/@totalcount)*100 FROM [dbo].[WHO_LifeExpectancy] WHERE age >= @mean-@sdev and age<= @mean+@sdev
SELECT @sigma2 = (count(*)/@totalcount)*100 FROM [dbo].[WHO_LifeExpectancy] WHERE age >= @mean-(2*@sdev) and age<= @mean+(2*@sdev)
SELECT @sigma3 = (count(*)/@totalcount)*100 FROM [dbo].[WHO_LifeExpectancy] WHERE age >= @mean-(3*@sdev) and age<= @mean+(3*@sdev)

SELECT @sigma1 AS 'Percentage in one SD FROM mean', @sigma2 AS 'Percentage in two SD FROM mean', 
@sigma3 as 'Percentage in 3 SD FROM mean'

The results I got are as below. As we can see ,68.31 % of data falls between one difference of mean and standard deviation, 95 % falls between two differences and 100% falls between 3. This is a near perfect normal distribution – one that lends itself to statistical analysis very easily.

To do the same analysis in R, I used script as below:

install.packages("RODBC")
library(RODBC)

cn <- odbcDriverConnect(connection="Driver={SQL Server Native Client 11.0};server=MALATH-PC\\SQL01;database=WorldHealth;Uid=sa;Pwd=Mysql1")
data <- sqlQuery(cn, 'select age from [dbo].[WHO_LifeExpectancy] where age is not null')
agemean<-mean(data$age)
agesd<-sd(data$age)
sigma1<-(sum(data$age>=agemean-agesd & data$age<=agemean+agesd)/length(data$age))*100
sigma2<-(sum(data$age>=agemean-(2*agesd) & data$age<=agemean+(2*agesd))/length(data$age))*100
sigma3<-(sum(data$age>=agemean-(3*agesd) & data$age<=agemean+(3*agesd))/length(data$age))*100
cat('Percentage in one SD FROM mean:',sigma1)
cat('Percentage in two SD FROM mean:',sigma2)
cat('Percentage in three SD FROM mean:',sigma3)

My results are as below:

rpercent

I can use the same script within TSQL and call it –

EXEC sp_execute_external_script
 @language = N'R'
 ,@script = N'agemean<-mean(InputDataSet$age)
agesd<-sd(InputDataSet$age)
sigma1<-(sum(InputDataSet$age>=agemean-agesd & InputDataSet$age<=agemean+agesd)/length(InputDataSet$age))*100
sigma2<-(sum(InputDataSet$age>=agemean-(2*agesd) & InputDataSet$age<=agemean+(2*agesd))/length(InputDataSet$age))*100
sigma3<-(sum(InputDataSet$age>=agemean-(3*agesd) & InputDataSet$age<=agemean+(3*agesd))/length(InputDataSet$age))*100
print(sigma1)
print(sigma2)
print(sigma3)'
 ,@input_data_1 = N'select age from [dbo].[WHO_LifeExpectancy] where age is not null;'

rpercentt

As we can see, the results are exactly the same in all 3 methods. We can also draw a bell curve with this data in R and see that it is a perfect curve, proving that the data is from a normally distributed set. The code I wrote to draw the graph is as below:

lower_bound <- agemean - agesd * 3
upper_bound <- agemean + agesd * 3
x <- seq(-3, 3, length = 1000) * agesd + agemean
y <- dnorm(x, agemean, agesd)
plot(x, y, type="n", xlab = "Life Expectancy", ylab = "", main = "Distribution of Life Expectancies", axes = FALSE)
lines(x, y)
sd_axis_bounds = 5
axis_bounds <- seq(-sd_axis_bounds * agesd + agemean, sd_axis_bounds * agesd + agemean, by = agesd)
axis(side = 1, at = axis_bounds, pos = 0)

graph

Multivariate Variable Analysis using R

So far I’ve worked on simple analytical techniques using one or two variables in a dataset. This article is a sort of a summary – about various techniques we can use for such datasets, depending on the type of variable in question. The techniques include – how to get summary statistics out as relevant, and how to plot the appropriate descriptive graph. There are fundamentally two types of variables –

1 Continues – variables which are not restricted in their value – such as height, weight, temperature etc.
2 Categorical – variables which can have specific values or a specific domain to which values belong.

The types of relationships we want to look at are

1 Categorical-Continues
2 Categorical-Categorical
3 Continues-Continues

1 Categorical and Continues Variable: For this purpose we will use one of R’s built in datasets, called Orange. This dataset has information on five types of orange trees, coded 1 to 5, with their circumference and age. The categorical variable here is the type of orange tree, for continues variable we can consider either circumference or age.

1 List the dataset. We can do this by simply typing Orange.
cc1

2 To get  the summary statistics on each type of tree - 
by(Orange$circumference,Orange$Tree, summary)

3 Now we want to understand what this data is made of. What are the min and max values , which is the biggest tree and which is the smallest, what is the overlap in values and so on. The easiest way to do this is to get the box-and-whisker plot of this data

boxplot(circumference~Tree, data=Orange, main="Circumference of Orange Trees",  xlab="Tree", ylab="Circumference", col=c("red","blue", "yellow","orange","darkgreen"))

cc3

From the plot we can see that type 3 trees have the smallest circumference while type 4 have the largest, with type 2 close to type 4. We can also see that type 1 trees have the thinnest dispersion of circumference while type 4 has the highest, closely followed by type 2. We can also see that there are no significant outliers in this data.

2 Relationship between two categorical variables:

For this purpose I can use the built in dataset in R called ‘HairEyeColor’. This dataset has gender wise hair and eye color for several people. Gender, Hair color, Eye color are categorical variables. For simplicity’s sake I am only going with gender and eye color.

Let us say we want the following information from this dataset –

1 % of men and women across eye color (and the unpivot – % of men and women per eye color)

2 % of men and women across hair color (and the unpivot – % of men and women per eye color)

3 Count of men and women in the mix (total)

4 Count of men and women for each eye/hair color

The code that accomplishes all of this is as below:

# Flatten the data into gender/eye color

gendereyemix<-xtabs(Freq~Sex+Eye,data.frame(HairEyeColor))

# % of men and women across eye color

prop.table(gendereyemix, 1)

# % of men and women for each specific eye color

prop.table(gendereyemix, 2)

# Number of men and women in the mix

margin.table(gendereyemix, 1)

# Number of men and women per eye color

margin.table(gendereyemix, 2)

Results are as below:

cc4

We can accomplish the same using 3 variables in the mix for both prop.table and margin.table functions.

We can also run many tests of independence with them.

The appropriate chart for comparing categorical variable may be bar chart.

barplot(gendereyemix, main="Gender-Eye Color Distribution",          xlab="Eye Color",col=c("darkblue","red"),         legend = rownames(gendereyemix))

cc5

3 Relationship between 2 continues variables:

For this let us consider the airquality dataframe, and the two continues variables it has, windspeed and temperature. To begin with we can get a summary of windspeed for each temperature by

by(airquality$Wind, airquality$Temp, summary)

cc6

Although this gives a decent idea of airspeeds at each temperature, we have no idea if there is any specific correlation between the two. To understand if there is a correlation –

t.test(airquality$Wind, airquality$Temp, data=airquality)

cc7

The p value is really small indicating that there may be a statistically significant correlation between airquality and temperature. We may want to draw a graph to understand what this correlation is – I tried a scatter plot

plot(airquality$Wind, airquality$Temp, main="Scatterplot of Wind versus Temperature", 
+ xlab="Wind ", ylab="Temperature", pch=19)

cc8

Now we add a line of best fit to the graph

lines(lowess(airquality$Wind,airquality$Temp), col="blue")

cc9

From this we can see that there is a correlation between temperature falling with increasing wind speed.

These are just a few examples of how to correlate variables. In many cases there may be a lot more than two variables in the mix and there are many strategies involved to study the correlation. But understand what types of variables they are, and what are best graphs to use for the correlation helps further our data analysis skills. Thanks for reading!

Associative Analytics: Two sample T Test

In the previous post we looked at a one way T-Test. A one way T Test helped us determine if a selected sample was indeed truly representative of the larger population. A Two way T Test goes a step further – it helps us determine if both samples came from the same population, or if their average mean equals zero.

I am using a similar example as I did in the earlier post – but with two people. There are two walkers – A and B. They want to know if having a heavy meal has any influence on walking. Below are how many steps they took the week following thanksgiving – I , by the way, did not indulge in a heavy thanksgiving meal, but she did. Our data is as below:

	Day 1	Day 2	Day 3	Day 4	Day 5	Day 6	Day 7
Walker A	10001	10300	10200	9900	10005	9900	10000
Walker B	8000	9200	8200	8900	9800	9900	9700

For applying the two sample T-test – we need the following conditions to be met:

1 There needs to be one continues dependant variable (walking steps) and one categorical independant variable – with two levels (whether walker had a heavy meal , or not).

2 The two samples are independant – they don’t walk together or at the same place.

3 The two samples follow normal distributions – there is no real, direct way to ensure this and there are many debates on how to determine it. For simplicity’s sake, i followed the example here – I came up with p values of under 0.05 for both walker A and B, so am good with proceeding with two sample t-test.

That done, let us define the null hypothesis, also called H0. Our null hypothesis here is that both samples have no mean difference, or in other words, the meal(s) had no influence on the differences in walking steps. The alternate, or opposite of that is that walker B walked less than walker A because he/she had a meal.

There are also parameters you need to pass to the T value function in R – if the variance of the two datasets is the same, you have to say that var.equal = true. In this case variance happens to be quite different, so we can safely leave that out and go with default T value function.

Below is the calls to the function – first from T, then TSQL, then R via TSQl. For R I did not choose to connect to SQL because the dataset in this case is seriously small.

I just open R studio and run code as below:

Using R:

a<-c(10001,10300,10200, 9900, 10005, 9900, 10000)
b<-c(8000, 9200,8200, 8900, 9800, 9900, 9700)
t.test(a,b)

My results are as below.

twowayttestr

I can do the same calculation of T value using T-SQL. I cannot calculate p value from TSQL as that comes from a table, but it is possible to look it up. I imported the set of values into a table called WalkingSteps with two columns, walkerAsteps and walkerBsteps. For doing the math on T value the formula stated here may be useful. My T-SQL code is as below

Using T-SQL:

/*TSQL to calculate T value*/
DECLARE @meanA FLOAT, @meanB FLOAT, @varA FLOAT, @varB FLOAT, @count int
SELECT @meanA = sum(walkerAsteps)/count(*) FROM WalkingSteps
SELECT @meanB = sum(walkerBsteps)/count(*) FROM WalkingSteps
SELECT @varA = var(walkerAsteps) FROM WalkingSteps
SELECT @varB = var(walkerBsteps) FROM WalkingSteps
SELECT @count = count(*) FROM WalkingSteps
SELECT 'T value:',((@meanA-@meanB)/sqrt((@varA/@count)+(@varB/@count)))

The result I get is as below:

tvalue

To get the corresponding P value I need to stick this into a calculator, like the one here.

tcalc

I get the same result I got with R – 0.17233. To be noted that I took the degrees of freedom from what R gave me – 6.46. If I had no access to R I’d go with 6 , as that makes logical sense. To understand more on degrees of freedom go here.

TSQL with R:

I can run the same exact R code from within TSQL and get the same results – as below:

EXEC sp_execute_external_script
 @language = N'R'
 ,@script = N'Tvalue<-t.test(InputDataSet$WalkerAsteps, InputDataSet$WalkerBsteps);
 print(Tvalue)'
 ,@input_data_1 = N'SELECT WalkerAsteps, WalkerBsteps FROM [WorldHealth].[dbo].[WalkingSteps];'

From the results we can see that the t-statistic is equal to 3.181 and the p-value is 0.007907,well below the 0.05 for the 95% confidence interval needed to accept the null hypothesis.Hence we conclude that with this data there is some evidence that a heavy meal does impact walking/exercise the week after.

I’d eat well, regardless :)) HAPPY THANKSGIVING :))

Associative Statistics – One sample T-Test with TSQL and R

In this post am going to attempt to explore a statistical procedure called ‘One Sample T Test’.

A T-Test is used to test the mean value of a data sample against a known mean of the entire population from where the sample came from. An example would be, if the average voting preference of the USA is Democrat, I want to test the average voting preference in KY to see if that corresponds to the national average. Another simpler example is if all coke bottles produced are 150 ml on an average – I want to see if this quantity is exactly true (on an average) of the 10 coke bottles I have in the fridge.

For this post I decided to go with a simple example of how many steps I walked with my per day for the month of August. My goal is 10,000 steps per day – that has been my average over the year but is this true of the data I gathered in August? I have a simple table with two columns – day and steps. Each record has how many steps I took in August per day, for 30 days. So – SELECT AVG(steps) FROM [dbo].[mala-steps] gives me 8262 as my average number of steps per day in August. I want to know if am consistently under performing my goal, or if this is a result of my being less active in August alone. Let me state my problem first – or state what is called ‘null hypothesis’:

I walk 10,000 steps on an average per year.

How is T value calculated? The formula for T value is a bit complex –

tvalue

The numerator x bar is the mean of the sample. The mew-zero as it is spelled is the hypothesised mean – or what I say I expect of the sample value – in my case , 10,000 steps.. The denominator – s, is the standard deviation of the sample or the square root of sum of difference between mean and each value, and n refers to sample size. Without pulling hair out on what this stands for etc – what it really means is the ratio of differences between sample values to the mean compared to ‘inner noise’, or the difference within the sample set itself. If you get a high value it means the sample set is probably not fitting my hypothesis, or there are too many differences between values and the hypothesised mean. A low value means the opposite, that the differences are internal to the sample.

My goal is to prove or disprove this with the sample selected from August. I am using a significance level of 0.01 or what is called 99% confidence level.

Using TSQL:

SELECT AVG(steps) AS 'MEAN' FROM [dbo].[mala-steps]
SELECT (AVG(STEPS)-10000)/(SQRT(VAR(STEPS))/SQRT(COUNT(*))) AS 'T VALUE' 
FROM [dbo].[mala-steps]

tsqltvalue

We get a mean of 8262.36 and a T value of -5.023.

Calculating the p value or probability for this T value can be done via calculator here – unfortunately this is not possible with TSQL. If we stick in the values of 5.023 and 29 degrees of freedom (30 values – 1) we get a really low p value

R does the entire math for us in one simple step as below:

install.packages("RODBC")
library(RODBC)
datasteps <- sqlQuery(cn, 'SELECT steps FROM [dbo].[mala-steps]') 
t.test(datasteps$steps, mu=10000, alternative="less", conf.level=0.95)

rttest

The same R code can be called from within TSQL too – giving results as below:

-- calculate one way T value
EXEC sp_execute_external_script
 @language = N'R'
 ,@script = N' tvalue <-t.test(InputDataSet$steps, mu=10000, alternative="less", conf.level=0.95);
 print(tvalue)'
 ,@input_data_1 = N'SELECT steps FROM [dbo].[mala-steps];'

rtsql-tvalue

From the output, we can see that the mean number of steps I have taken for the month of August is 8262. The t value is 5.02 (the sign does not matter). Which means that difference between individual values and the mean is higher than the ‘inner noise’ or the difference between values in the sample sample set.

The p-value ,or the probability of getting this t value from this sample is really really low – 1.187e-05.So, it is more likely this dataset/sample is probably not a good one – or in other words, i can’t accept the null hypothesis that I did walk an average of 10,000 steps a day based on this sample. Maybe August was not the best month to judge my commitment to exercise…or maybe i’d have to try more samples than August alone! More on those in next post!

Statistics with TSQL and R: Chi Square Test

As I move on from descriptive and largely univariate (one variable based) analysis of data into more multivariate data – one of the first data analysis tests that came to mind is the Chi Square Test. It is a very commonly used test to understand relationships between two variables that are largely categorical in nature. The Chi Square test is often used in clinical trials or experiments that have a before and after analysis of their subjects needed.

For my purpose I downloaded a dataset from here – this is a study of lung conditions and respiratory illnesses among a group of children in Austria. There are many categories here of the conditions these kids were subject to. I summarized the data for my chi square test – and I get a small dataset as below of kids who had a parent who smoked in the home and who were sick. So my two categorical variables are – parent who smoked and did not, and kids who are sick and not. The frequencies I pulled are as below –

Exposure	Sick	Notsick
YES	250	323
NO	401	575

I created a table as below and put the data into it.

CREATE TABLE [dbo].[Respdisease_summary](
 [Exposure] [varchar](5) NULL,
 [Sick] [decimal](18, 2) NULL,
 [Notsick] [decimal](18, 2) NULL
) ON [PRIMARY]

For any dataset to lend itself to the Chi Square test it has to fit the following conditions –

1 Both variables are categorical (in this case – exposure to smoking – yes/no, and health condition – sick/not sick are both categorical).
2 Researchers used a random sample to collect data.
3 Researchers had an adequate sample size.Generally the sample size should be at least 100.
4 The number of respondents in each cell should be at least 5.

My dataset appears to fit these conditions, so I proceeded with analysing the data. Step 1 :Now that I have the data , I need to come up with a statement of what am trying to establish by doing these tests. Or in other words , a hypothesis. In statistical terms the logic is the similar to ‘innocent until proven guilty’ . Or in other words, my hypothesis in this case is that there is no correlation between parents who smoke at home and kids who suffer respiratory illness. My goal is to find out if this is true with 95% certainity(another common standard is what percentage of certainity, also called level of confidence)

Step 2: Next, I need to come up with degrees of freedom for this grid of data. Degrees of freedom in a nutshell means how many cells/data elements are actually independant versus how many are dependant on the one or ones already filled. A detailed discussion on this concept can be found here. You don’t have to understand it in a lot of detail but just enough to use the simple formula –
DF = (# of rows – 1) * (# of columns – 1). In this case – it is (2-1)*(2-1) which is actually 1.

Step 3: I need to create a table of expected values of illness, as opposed to exposed which we got from the data.By that I mean what would value be if the kid was not exposed to the condition – or if our hypothesis that there is no connection just happened to be true?

For each cell in my data, the expected value for each cell of data – is the (row total/sum of all cells)*column total for the cell. I know am using excel like terms here but it is only to make the concept simple. (It is very possible to do this in excel by the way, but am restricting my scope to TSQL and R).

Step 4: Once I get the table of exposed and expected – i need to compare both and arrive at the chi-square value – which is (square of sum (exposed-expected))/sum(expected). Whether I choose to do this on each cell or add all the exposed and expected cell values and calculate it once – really does not matter.

Step 5: After I get the value of chi squared – I can use a table to get the probability of the two variables being correlated, given this chi square value. If you use R it will do this additional step for you and give you the p-value, or probablity as it is called. With other tools you may have to use a calculator like here. If the p value <= 0.05 (which comes fsrom our 95% confidence interval), the value is statistically significant and the null hypothesis is valid. If not, as it is in our case – where the value is 0.3274 – there is no statistically significant correlation for the null hypothesis. So it is safe to conclude that the opposite may be true with 95 percent certainity.

Now, on to the 3 ways of achieving this :

TSQL – I have not used the most optimal T-SQL here. The goal is clarity and I have taken a very methodical step-by-step approach.

CREATE TABLE #expected 
 (Exposure varchar(5), sick decimal(18,4), notsick decimal(18, 4))
 INSERT INTO #expected
 SELECT 'Yes', (A.Row1Total/E.Totalkids)*B.Col1Total as ExpectedSick,
 (A.Row1Total/E.Totalkids) * c.Col2Total as ExpectedNotsick
 FROM 
 (SELECT sick+notsick AS Row1Total FROM [dbo].[Respdisease_summary] where exposure = 'Yes') as A,
 (SELECT sum(sick) AS Col1Total FROM [dbo].[Respdisease_summary]) as b,
 (SELECT sum(notsick) AS Col2Total FROM [dbo].[Respdisease_summary]) as c,
 (SELECT sum(sick) + sum(notsick) AS Totalkids FROM [dbo].[Respdisease_summary]) AS E

INSERT INTO #expected
 SELECT 'No', (D.Row2Total/E.Totalkids)*B.Col1Total
 , (D.Row2Total/E.Totalkids) * c.Col2Total
 FROM 
 (SELECT sum(sick) AS Col1Total FROM [dbo].[Respdisease_summary]) as b,
 (SELECT sum(notsick) AS Col2Total FROM [dbo].[Respdisease_summary]) as c,
 (SELECT sum(sick) + sum(notsick) AS Totalkids FROM [dbo].[Respdisease_summary]) AS E,
 (SELECT sick+notsick AS Row2Total FROM [dbo].[Respdisease_summary] WHERE exposure = 'No') as D

SELECT SQUARE(a.actual-b.expected)/b.expected FROM 
 (SELECT sum(sick+notsick) as actual FROM [dbo].[Respdisease_summary]) A,
 (SELECT SUM(sick+notsick) as expected FROM #expected) B

CREATE TABLE #chisquare 
 (Exposure varchar(5), sick decimal(18,4), notsick decimal(18, 4))

INSERT INTO #chisquare
 SELECT 'yes',square(A.actualsick-B.expectedsick)/B.expectedsick,square(C.actualnotsick-D.expectednotsick)/D.expectednotsick 
 FROM
 (SELECT sick as Actualsick FROM [dbo].[Respdisease_summary] where exposure = 'Yes') as A,
 (SELECT sick as Expectedsick FROM [dbo].[#expected] where exposure = 'Yes') as B,
(SELECT notsick as Actualnotsick FROM [dbo].[Respdisease_summary] where exposure = 'Yes') as C,
 (SELECT notsick as Expectednotsick FROM [dbo].[#expected] where exposure = 'Yes') as D

INSERT INTO #chisquare
 SELECT 'No',square(A.actualsick-B.expectedsick)/B.expectedsick,square(C.actualnotsick-D.expectednotsick)/D.expectednotsick 
 FROM
 (SELECT sick as Actualsick FROM [dbo].[Respdisease_summary] where exposure = 'No') as A,
 (SELECT sick as Expectedsick FROM [dbo].[#expected] where exposure = 'No') as B,
 (SELECT notsick as Actualnotsick FROM [dbo].[Respdisease_summary] where exposure = 'No') as C,
 (SELECT notsick as Expectednotsick FROM [dbo].[#expected] where exposure = 'No') as D

SELECT SUM(sick) + sum(notsick) As CHISQUARE FROM #chisquare

The result I got was as below – a chi square value of 0.9590. If I look up the calculator for this chi square value with 1 degree of freedom – I get a probability of 0.3274.

chisqtsql

R: It is with problems like this that you really get to appreciate the efficiency of R. Just two little steps, easy-peasy and you get all that went into those multiple lines of TSQL code.

#Install basic packages
install.packages("RODBC")
library(RODBC)
#Connect to server and retrieve data
cn <- odbcDriverConnect(connection="Driver={SQL Server Native Client 11.0};server=MALATH-PC\\SQL01;database=WorldHealth;Uid=sa;Pwd=<mypwd>")
data1 <- sqlQuery(cn, ' select Sick,Notsick from dbo.Respdisease_summary')
#Calculate the chi square value
chisqt<-chisq.test(data1[1:2,c("Sick","Notsick")],correct=FALSE)
chisqt

I get results as below – R neatly does the calculation of P-value too for us.

chisqr

3 R Code from within of TSQL. This is my favorite part. Now you can tap into what R does from within of TSQL itself with a few simple lines of code as below.

EXEC sp_execute_external_script
 @language = N'R'
 ,@script = N'x<-chisq.test(InputDataSet[1:2,c("Sick","Notsick")], correct = FALSE);print(x)'
 ,@input_data_1 = N'SELECT Sick,Notsick FROM [WorldHealth].[dbo].[Respdisease_summary]'

And bingo, same results as 2.

chisqrtsql

There is a correction called Yates continuity correction that sometimes needs to be applied if our degrees of freedom is just one. R by default uses this coefficient which text book wise is jus t subtracting 0.5 from expected values. But for some reason I could not get the values to tie up using manual math/tsql and R upon usage of this, so I turned it off by saying ‘correct = False’ in R statement. It is also noteworthy that Chi SQuare test tells you nothing about the actual relationship between variables – it is only a test of independance and can tell you about degree of dependancy , that is all.

Statistics with T-SQL and R – the Pearson’s Correlation Coefficient

In this post I will attempt to explore calculation of a very basic statistic based on linear relationship between two variables. That is, a number that tells you if two numeric variables in a dataset are possibly correlated and if yes, by what degree. The Pearson’s coefficient is a number that attempts to measure this relationship.

The dataset I am using for this is downloaded from here. It is a dataset related to gender development index from various parts of the world. I am not using all the variables here. I am only attempting to examine if there is a correlation between average number of years in school, and gross national income, for women. Or in other words – Do women stay longer or shorter in schools because of income reasons? One important thing to be aware as we study this – is that correlation does not mean causation. By that – if I find a correlation between number of years women spend in school and the income, does not really mean the two are directly related. There may be many other factors influencing this relationship – or, after studying all the other factors we may very well conclude that there is not a significant relationship one way or the other. But, it is a starting point to see if there is any correlation between these two variables across countries.

The statistical definition of Pearson’s R Coefficient, as it is called, can be found in detail here for those interested. A value of 1 indicates that there is a strong positive correlation(the two variables in question increase together), 0 indicates no correlation between them, and -1 indicates a strong negative correlation (the two variables decrease together). But you rarely get a perfect -1, 0 or 1. Most values are fractional and interpreted as follows:
High correlation: .5 to 1.0 or -0.5 to 1.0.
Medium correlation: .3 to .5 or -0.3 to .5.
Low correlation: .1 to .3 or -0.1 to -0.3.

Now, let us look at calculation this value for the two variables we have chosen using R, then T-SQL and then R script within T-SQL.
Using R:

install.packages(“RODBC”)
library(RODBC)
mydata <- sqlQuery(cn, ‘select [FemaleMeanYearsofSchooling2014],
[GNAPerCapitaWomen2014]
FROM [WorldHealth].[dbo].[GenderDevelopmentIndex] WHERE [femalemeanyearsofschooling2014] >0 ‘)

cor(mydata)

R creates a matrix out the two columns of data and correlates it four ways. I have only highlighted what is relevant to us – a value of 0.64, which suggests a moderately strong correlation.

The same calculation can be done with T-SQL. It is not as simple or elegant as it is in R, but it is very doable. To understand the formula read here. Although it is very possible to do this in one step, I have broken it up into 4 steps to help with clarity and understanding.

TSQL Code:

DECLARE @PART1 FLOAT, @PART2 FLOAT, @PART3 FLOAT, @PART4 FLOAT
SELECT @PART1 = SUM([FemaleSchoolingyears2014]*[GNAPerCapitaWomen2014])
FROM [dbo].[GenderDevelopmentIndex] where [FemaleSchoolingyears2014] > 0

SELECT @PART2 = (SUM([FemaleSchoolingyears2014])*SUM([GNAPerCapitaWomen2014]))/count(*)
FROM [dbo].[GenderDevelopmentIndex] where [FemaleSchoolingyears2014] > 0

SELECT @PART3 = SQRT(SUM([FemaleSchoolingyears2014]*[FemaleSchoolingyears2014])
– (SUM([FemaleSchoolingyears2014])*SUM([FemaleSchoolingyears2014]))/count(*))
FROM [dbo].[GenderDevelopmentIndex] where [FemaleSchoolingyears2014] > 0

SELECT @PART4 = SQRT(SUM([GNAPerCapitaWomen2014]*[GNAPerCapitaWomen2014])
– (SUM([GNAPerCapitaWomen2014])*SUM([GNAPerCapitaWomen2014]))/count(*))
FROM [dbo].[GenderDevelopmentIndex] where [FemaleSchoolingyears2014] > 0

SELECT ‘Pearsons correlation coefficient=’,ROUND((@PART1-@PART2)/(@PART3*@PART4), 2)

The results I get are as below:

cor-TSQL

So far, in both cases the value is a good 0.64. Now , instead of performing all this math in T-SQL we can call the R function to do it in one line, within T-SQL.

R call from within T-SQL:

EXEC sp_execute_external_script
@language = N’R’
,@script = N’ cor <-cor(InputDataSet$FemaleMeanYearsofSchooling2014,InputDataSet$GNAPerCapitaWomen2014);
print(cor);’
,@input_data_1 = N’select FemaleMeanYearsofSchooling2014,GNAPerCapitaWomen2014 FROM [WorldHealth].[dbo].[GenderDevelopmentIndex] where [FemaleSchoolingyears2014] > 0
;’;

My results are as below:

cor-r-tsql

As we can see, we got the same results in all 3 cases. The 0.64 correlation indicates the possibility of a moderately strong correlation between length of years women are in school and the GDP ratio of the country. It does not mean the GDP factor is the cause but merely a possibility. There is a old post authored by Joe Celko here that refers to the same calculation and can also help with conceptual understanding.

Thanks for reading.

Descriptive Statistics with SQL and R – II

In the previous post I looked into some very basic and common measures of descriptive statistics – mean, median and mode, and how to derive these using T-SQL, R as well as a combo of the two in SQL Server 2016. These measures also called measures of ‘Central Tendency‘. In this post am going to describe some measures called ‘Measures of Dispersion‘. Dispersion refers to how much the said value differs from the average and how many such values are distributed around the average.

Below are 4 very common measures of dispersion –

Range
Inter-Quartile Range
Variance
Standard Deviation

The first and most common measure of dispersion is called ‘Range‘. The range is just the difference between the maximum and minimum values in the dataset. It tells you how much gap there is between the two and therefore how wide the dataset is in terms of its values. It is however, quite misleading when you have outliers in the data. If you have one value that is very large or very small that can skew the Range and does not really mean you have values spanning the minimum to the maximum.

To lower this kind of an issue with outliers – a second variation of the range called Inter-Quartile Range, or IQR is used. The IQR is calculated by dividing the dataset into 4 equal parts after sorting the said value in ascending order. For the first and third part, the maximum values are taken and then subtracted from each other. The IQR ensures that you are looking at top and near-bottom ranges and therefore the value it gives is probably spanning the range.

A more accurate calculation of dispersion is called Variance. Variance is the average of the difference between each value and the mean, divided by the number of values. When your dataset is comprised of an entire population the number of values are the total number of values, but when it is comprised of a sample, the number of values are deducted by 1 to ensure a statistical adjustment. The larger the variance, the more widespread the values are.

The last measure of dispersion am going to look into in this post is Standard Deviation.The Standard Deviation is the square root of the variance. The smaller the standard deviation, the lesser is the dispersion around the mean. The larger, the greater.

The higher the values of variance and standard deviation – the more skewed your data is and the less likely it is to lend itself to any real statistical analysis or even forecasting or predictive reporting.

I used the same data set that I did in the earlier post to run queries for this. Details on that can be found here.

Using T-SQL:

Below are my T-SQL queries to derive range, inter quartile range, variance and standard deviation. I was surprised to find that T-SQL actually has a built in function for variance, which makes it very easy. Was not sure why this particular function would exist when the vast majority of other statistical functions don’t, but makes sense to use it since it does 🙂

–Calculate range of the dataset
SELECT MAX(AGE) – MIN(AGE) as Range FROM [dbo].[WHO_LifeExpectancy]

–Calculate Interquartile range of the dataset
SELECT MAX(QUARTILEMAX) – MIN(QUARTILEMAX) AS INTERQUARTILERANGE
FROM
(SELECT QUARTILE, MAX(AGE) AS QUARTILEMAX FROM (SELECT NTILE(4) OVER ( ORDER BY Age ) AS Quartile ,
Age FROM [dbo].[WHO_LifeExpectancy]) A
GROUP BY QUARTILE HAVING QUARTILE = 1 OR QUARTILE = 3) A

–Calculate variance of the datasest
SELECT VAR(Age) AS VARIANCE FROM [dbo].[WHO_LifeExpectancy]

–Calculate standard deviation of the dataset
SELECT SQRT(VAR(Age)) AS STDDEV FROM [dbo].[WHO_LifeExpectancy]

Results are as below:

descstats2

Using R:

The R script to do the exact same thing is as below. None of these calculations require anything more than calling a packaged function in R, which is why it is usually the preferred way to do it.

#load necessary libraries and connect to the database
install.packages(“RODBC”)
library(RODBC)

cn <- odbcDriverConnect(connection=”Driver={SQL Server Native Client 11.0};server=MALATH-PC\\SQL01;database=WorldHealth;Uid=sa;Pwd=<password>”)
data <- sqlQuery(cn, ‘select age from [dbo].[WHO_LifeExpectancy] where age is not null’)

#Get range
agerange <- max(data)-min(data)
agerange

#Get Interquartile Range
interquartileagerange <- IQR(unlist(data))
interquartileagerange

#Get Variance
variance <- var(data)
variance

#Get Standard Deviation
stddev <- sd(unlist(data))
stddev

The results you get, almost exact matches to what T-SQL gave us:

descstats2-R

3. Using R function calls with T-SQL

Now the last part , where we call the R script via T-SQL. We have to make small changes to the script – the ‘cat’ function to concatenate results, /n to introduce a carriage return for readability and removing the ‘unlist’ that native R needs because of how R structures data it reads directly from SQL . But for this the math is exactly the same.

— calculate simple dispersion measures
EXEC sp_execute_external_script
@language = N’R’
,@script = N’ range <-max(InputDataSet$LifeExpectancies)-min(InputDataSet$LifeExpectancies)
cat(“Range”, range,”\n”);
InterquartileRange <-IQR(InputDataSet$LifeExpectancies)
cat(“InterQuartileRange”, InterquartileRange,”\n”);
variance <-var(InputDataSet$LifeExpectancies)
cat(“Variance”, variance,”\n”);
stdeviation <-sd(InputDataSet$LifeExpectancies)
cat(“StdDeviation”, stdeviation,”\n”);’
,@input_data_1 = N’SELECT LifeExpectancies = Age FROM [WorldHealth].[dbo].[WHO_LifeExpectancy];’
;

And the results, exactly the same as the two above:

descstats2-sqlandr

It has been fun so far doing this in 3 different ways and getting the same results. But as we go into more advanced statistics it will not be that simple. It will, however, definitely help us understand how far we can push statistical math with T-SQL, how easy and simple it is with R, and what can/cannot be done in the first version of calling R from within T-SQL. I can’t wait to play with it further and blog as I go along.

Thank you for reading.

Script to create demo database and load data for statistics and R

Make sure you have a working install of SQL Server 2016. The size of the database is only 8 MB.

USE [master]
GO

/****** Object: Database [WorldHealth] Script Date: 7/15/2016 4:44:58 PM ******/
CREATE DATABASE [WorldHealth]
CONTAINMENT = NONE
ON PRIMARY
( NAME = N’WorldHealth’, FILENAME = N’D:\DATA\WorldHealth.mdf’ , SIZE = 8192KB , MAXSIZE = UNLIMITED, FILEGROWTH = 65536KB )
LOG ON
( NAME = N’WorldHealth_log’, FILENAME = N’D:\Log\WorldHealth_log.ldf’ , SIZE = 8192KB , MAXSIZE = 2048GB , FILEGROWTH = 65536KB )
GO

ALTER DATABASE [WorldHealth] SET COMPATIBILITY_LEVEL = 130
GO

USE [WorldHealth]
GO
/****** Object: Table [dbo].[WHO_LifeExpectancy] Script Date: 7/15/2016 4:41:49 PM ******/
SET ANSI_NULLS ON
GO
SET QUOTED_IDENTIFIER ON
GO
SET ANSI_PADDING ON
GO
CREATE TABLE [dbo].[WHO_LifeExpectancy](
[Country] [varchar](50) NULL,
[Year] [varchar](50) NULL,
[Gender] [varchar](50) NULL,
[Age] [numeric](18, 2) NULL
) ON [PRIMARY]

GO
SET ANSI_PADDING OFF
GO
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Afghanistan’, N’2000′, N’Male’, CAST(45.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Albania’, N’2000′, N’Male’, CAST(61.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Algeria’, N’2000′, N’Male’, CAST(61.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Angola’, N’2000′, N’Male’, CAST(38.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Antigua and Barbuda’, N’2000′, N’Male’, CAST(63.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Argentina’, N’2000′, N’Male’, CAST(62.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Armenia’, N’2000′, N’Male’, CAST(61.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Australia’, N’2000′, N’Male’, CAST(67.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Austria’, N’2000′, N’Male’, CAST(66.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Azerbaijan’, N’2000′, N’Male’, CAST(57.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bahamas’, N’2000′, N’Male’, CAST(61.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bahrain’, N’2000′, N’Male’, CAST(64.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bangladesh’, N’2000′, N’Male’, CAST(56.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Barbados’, N’2000′, N’Male’, CAST(63.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Belarus’, N’2000′, N’Male’, CAST(57.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Belgium’, N’2000′, N’Male’, CAST(65.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Belize’, N’2000′, N’Male’, CAST(58.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Benin’, N’2000′, N’Male’, CAST(47.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bhutan’, N’2000′, N’Male’, CAST(53.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bolivia (Plurinational State of)’, N’2000′, N’Male’, CAST(54.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bosnia and Herzegovina’, N’2000′, N’Male’, CAST(62.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Botswana’, N’2000′, N’Male’, CAST(42.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Brazil’, N’2000′, N’Male’, CAST(59.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Brunei Darussalam’, N’2000′, N’Male’, CAST(66.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bulgaria’, N’2000′, N’Male’, CAST(60.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Burkina Faso’, N’2000′, N’Male’, CAST(42.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Burundi’, N’2000′, N’Male’, CAST(41.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cote d”Ivoire’, N’2000′, N’Male’, CAST(41.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cabo Verde’, N’2000′, N’Male’, CAST(60.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cambodia’, N’2000′, N’Male’, CAST(42.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cameroon’, N’2000′, N’Male’, CAST(43.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Canada’, N’2000′, N’Male’, CAST(68.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Central African Republic’, N’2000′, N’Male’, CAST(39.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Chad’, N’2000′, N’Male’, CAST(39.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Chile’, N’2000′, N’Male’, CAST(65.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’China’, N’2000′, N’Male’, CAST(63.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Colombia’, N’2000′, N’Male’, CAST(59.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Comoros’, N’2000′, N’Male’, CAST(50.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Congo’, N’2000′, N’Male’, CAST(45.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Costa Rica’, N’2000′, N’Male’, CAST(66.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Croatia’, N’2000′, N’Male’, CAST(63.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cuba’, N’2000′, N’Male’, CAST(66.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cyprus’, N’2000′, N’Male’, CAST(67.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Czech Republic’, N’2000′, N’Male’, CAST(63.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Democratic People”s Republic of Korea’, N’2000′, N’Male’, CAST(55.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Democratic Republic of the Congo’, N’2000′, N’Male’, CAST(42.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Denmark’, N’2000′, N’Male’, CAST(66.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Djibouti’, N’2000′, N’Male’, CAST(49.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Dominican Republic’, N’2000′, N’Male’, CAST(61.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ecuador’, N’2000′, N’Male’, CAST(61.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Egypt’, N’2000′, N’Male’, CAST(58.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’El Salvador’, N’2000′, N’Male’, CAST(54.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Equatorial Guinea’, N’2000′, N’Male’, CAST(45.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Eritrea’, N’2000′, N’Male’, CAST(34.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Estonia’, N’2000′, N’Male’, CAST(59.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ethiopia’, N’2000′, N’Male’, CAST(42.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Fiji’, N’2000′, N’Male’, CAST(59.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Finland’, N’2000′, N’Male’, CAST(65.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’France’, N’2000′, N’Male’, CAST(67.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Gabon’, N’2000′, N’Male’, CAST(51.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Gambia’, N’2000′, N’Male’, CAST(48.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Georgia’, N’2000′, N’Male’, CAST(61.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Germany’, N’2000′, N’Male’, CAST(66.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ghana’, N’2000′, N’Male’, CAST(49.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Greece’, N’2000′, N’Male’, CAST(67.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Grenada’, N’2000′, N’Male’, CAST(60.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guatemala’, N’2000′, N’Male’, CAST(53.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guinea’, N’2000′, N’Male’, CAST(45.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guinea-Bissau’, N’2000′, N’Male’, CAST(44.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guyana’, N’2000′, N’Male’, CAST(55.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Haiti’, N’2000′, N’Male’, CAST(49.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Honduras’, N’2000′, N’Male’, CAST(60.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Hungary’, N’2000′, N’Male’, CAST(60.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Iceland’, N’2000′, N’Male’, CAST(69.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’India’, N’2000′, N’Male’, CAST(53.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Indonesia’, N’2000′, N’Male’, CAST(58.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Iran (Islamic Republic of)’, N’2000′, N’Male’, CAST(61.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Iraq’, N’2000′, N’Male’, CAST(59.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ireland’, N’2000′, N’Male’, CAST(65.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Israel’, N’2000′, N’Male’, CAST(68.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Italy’, N’2000′, N’Male’, CAST(68.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Jamaica’, N’2000′, N’Male’, CAST(62.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Japan’, N’2000′, N’Male’, CAST(70.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Jordan’, N’2000′, N’Male’, CAST(62.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kazakhstan’, N’2000′, N’Male’, CAST(53.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kenya’, N’2000′, N’Male’, CAST(44.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kiribati’, N’2000′, N’Male’, CAST(54.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kuwait’, N’2000′, N’Male’, CAST(63.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kyrgyzstan’, N’2000′, N’Male’, CAST(56.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lao People”s Democratic Republic’, N’2000′, N’Male’, CAST(49.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Latvia’, N’2000′, N’Male’, CAST(58.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lebanon’, N’2000′, N’Male’, CAST(59.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lesotho’, N’2000′, N’Male’, CAST(42.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Liberia’, N’2000′, N’Male’, CAST(43.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Libya’, N’2000′, N’Male’, CAST(61.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lithuania’, N’2000′, N’Male’, CAST(59.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Luxembourg’, N’2000′, N’Male’, CAST(66.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Madagascar’, N’2000′, N’Male’, CAST(49.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Malawi’, N’2000′, N’Male’, CAST(36.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Malaysia’, N’2000′, N’Male’, CAST(62.50 AS Numeric(18, 2)))
GO
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Maldives’, N’2000′, N’Male’, CAST(61.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mali’, N’2000′, N’Male’, CAST(43.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Malta’, N’2000′, N’Male’, CAST(67.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mauritania’, N’2000′, N’Male’, CAST(51.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mauritius’, N’2000′, N’Male’, CAST(61.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mexico’, N’2000′, N’Male’, CAST(64.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Micronesia (Federated States of)’, N’2000′, N’Male’, CAST(59.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mongolia’, N’2000′, N’Male’, CAST(54.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Montenegro’, N’2000′, N’Male’, CAST(62.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Morocco’, N’2000′, N’Male’, CAST(59.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mozambique’, N’2000′, N’Male’, CAST(41.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Myanmar’, N’2000′, N’Male’, CAST(53.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Namibia’, N’2000′, N’Male’, CAST(49.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Nepal’, N’2000′, N’Male’, CAST(54.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Netherlands’, N’2000′, N’Male’, CAST(67.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’New Zealand’, N’2000′, N’Male’, CAST(67.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Nicaragua’, N’2000′, N’Male’, CAST(48.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Niger’, N’2000′, N’Male’, CAST(43.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Nigeria’, N’2000′, N’Male’, CAST(40.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Norway’, N’2000′, N’Male’, CAST(67.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Oman’, N’2000′, N’Male’, CAST(62.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Pakistan’, N’2000′, N’Male’, CAST(54.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Panama’, N’2000′, N’Male’, CAST(64.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Papua New Guinea’, N’2000′, N’Male’, CAST(51.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Paraguay’, N’2000′, N’Male’, CAST(61.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Peru’, N’2000′, N’Male’, CAST(57.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Philippines’, N’2000′, N’Male’, CAST(56.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Poland’, N’2000′, N’Male’, CAST(62.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Portugal’, N’2000′, N’Male’, CAST(65.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Qatar’, N’2000′, N’Male’, CAST(64.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Republic of Korea’, N’2000′, N’Male’, CAST(65.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Republic of Moldova’, N’2000′, N’Male’, CAST(57.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Romania’, N’2000′, N’Male’, CAST(60.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Russian Federation’, N’2000′, N’Male’, CAST(53.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Rwanda’, N’2000′, N’Male’, CAST(35.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Saint Lucia’, N’2000′, N’Male’, CAST(62.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Saint Vincent and the Grenadines’, N’2000′, N’Male’, CAST(61.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Samoa’, N’2000′, N’Male’, CAST(61.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sao Tome and Principe’, N’2000′, N’Male’, CAST(53.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Saudi Arabia’, N’2000′, N’Male’, CAST(61.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Senegal’, N’2000′, N’Male’, CAST(49.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Serbia’, N’2000′, N’Male’, CAST(62.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Seychelles’, N’2000′, N’Male’, CAST(61.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sierra Leone’, N’2000′, N’Male’, CAST(33.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Singapore’, N’2000′, N’Male’, CAST(68.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Slovakia’, N’2000′, N’Male’, CAST(61.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Slovenia’, N’2000′, N’Male’, CAST(63.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Solomon Islands’, N’2000′, N’Male’, CAST(58.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Somalia’, N’2000′, N’Male’, CAST(42.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’South Africa’, N’2000′, N’Male’, CAST(48.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’South Sudan’, N’2000′, N’Male’, CAST(41.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Spain’, N’2000′, N’Male’, CAST(66.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sri Lanka’, N’2000′, N’Male’, CAST(61.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sudan’, N’2000′, N’Male’, CAST(49.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Suriname’, N’2000′, N’Male’, CAST(57.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Swaziland’, N’2000′, N’Male’, CAST(41.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sweden’, N’2000′, N’Male’, CAST(68.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Switzerland’, N’2000′, N’Male’, CAST(67.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Syrian Arab Republic’, N’2000′, N’Male’, CAST(60.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Tajikistan’, N’2000′, N’Male’, CAST(53.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Thailand’, N’2000′, N’Male’, CAST(61.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Macedonia’, N’2000′, N’Male’, CAST(62.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Timor-Leste’, N’2000′, N’Male’, CAST(50.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Togo’, N’2000′, N’Male’, CAST(47.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Tonga’, N’2000′, N’Male’, CAST(63.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Trinidad and Tobago’, N’2000′, N’Male’, CAST(59.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Tunisia’, N’2000′, N’Male’, CAST(62.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Turkey’, N’2000′, N’Male’, CAST(59.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Turkmenistan’, N’2000′, N’Male’, CAST(54.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Uganda’, N’2000′, N’Male’, CAST(39.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ukraine’, N’2000′, N’Male’, CAST(56.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United Arab Emirates’, N’2000′, N’Male’, CAST(64.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United Kingdom’, N’2000′, N’Male’, CAST(67.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United Republic of Tanzania’, N’2000′, N’Male’, CAST(42.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United States of America’, N’2000′, N’Male’, CAST(65.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Uruguay’, N’2000′, N’Male’, CAST(63.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Uzbekistan’, N’2000′, N’Male’, CAST(57.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Vanuatu’, N’2000′, N’Male’, CAST(60.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Venezuela’, N’2000′, N’Male’, CAST(60.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Viet Nam’, N’2000′, N’Male’, CAST(59.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Yemen’, N’2000′, N’Male’, CAST(52.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Zambia’, N’2000′, N’Male’, CAST(36.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Zimbabwe’, N’2000′, N’Male’, CAST(39.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Afghanistan’, N’2000′, N’Male’, CAST(45.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Albania’, N’2000′, N’Male’, CAST(61.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Algeria’, N’2000′, N’Male’, CAST(61.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Angola’, N’2000′, N’Male’, CAST(38.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Antigua and Barbuda’, N’2000′, N’Male’, CAST(63.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Argentina’, N’2000′, N’Male’, CAST(62.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Armenia’, N’2000′, N’Male’, CAST(61.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Australia’, N’2000′, N’Male’, CAST(67.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Austria’, N’2000′, N’Male’, CAST(66.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Azerbaijan’, N’2000′, N’Male’, CAST(57.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bahamas’, N’2000′, N’Male’, CAST(61.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bahrain’, N’2000′, N’Male’, CAST(64.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bangladesh’, N’2000′, N’Male’, CAST(56.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Barbados’, N’2000′, N’Male’, CAST(63.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Belarus’, N’2000′, N’Male’, CAST(57.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Belgium’, N’2000′, N’Male’, CAST(65.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Belize’, N’2000′, N’Male’, CAST(58.90 AS Numeric(18, 2)))
GO
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Benin’, N’2000′, N’Male’, CAST(47.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bhutan’, N’2000′, N’Male’, CAST(53.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bolivia (Plurinational State of)’, N’2000′, N’Male’, CAST(54.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bosnia and Herzegovina’, N’2000′, N’Male’, CAST(62.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Botswana’, N’2000′, N’Male’, CAST(42.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Brazil’, N’2000′, N’Male’, CAST(59.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Brunei Darussalam’, N’2000′, N’Male’, CAST(66.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bulgaria’, N’2000′, N’Male’, CAST(60.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Burkina Faso’, N’2000′, N’Male’, CAST(42.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Burundi’, N’2000′, N’Male’, CAST(41.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cote d”Ivoire’, N’2000′, N’Male’, CAST(41.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cabo Verde’, N’2000′, N’Male’, CAST(60.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cambodia’, N’2000′, N’Male’, CAST(42.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cameroon’, N’2000′, N’Male’, CAST(43.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Canada’, N’2000′, N’Male’, CAST(68.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Central African Republic’, N’2000′, N’Male’, CAST(39.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Chad’, N’2000′, N’Male’, CAST(39.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Chile’, N’2000′, N’Male’, CAST(65.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’China’, N’2000′, N’Male’, CAST(63.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Colombia’, N’2000′, N’Male’, CAST(59.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Comoros’, N’2000′, N’Male’, CAST(50.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Congo’, N’2000′, N’Male’, CAST(45.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Costa Rica’, N’2000′, N’Male’, CAST(66.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Croatia’, N’2000′, N’Male’, CAST(63.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cuba’, N’2000′, N’Male’, CAST(66.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cyprus’, N’2000′, N’Male’, CAST(67.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Czech Republic’, N’2000′, N’Male’, CAST(63.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Democratic People”s Republic of Korea’, N’2000′, N’Male’, CAST(55.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Democratic Republic of the Congo’, N’2000′, N’Male’, CAST(42.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Denmark’, N’2000′, N’Male’, CAST(66.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Djibouti’, N’2000′, N’Male’, CAST(49.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Dominican Republic’, N’2000′, N’Male’, CAST(61.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ecuador’, N’2000′, N’Male’, CAST(61.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Egypt’, N’2000′, N’Male’, CAST(58.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’El Salvador’, N’2000′, N’Male’, CAST(54.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Equatorial Guinea’, N’2000′, N’Male’, CAST(45.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Eritrea’, N’2000′, N’Male’, CAST(34.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Estonia’, N’2000′, N’Male’, CAST(59.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ethiopia’, N’2000′, N’Male’, CAST(42.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Fiji’, N’2000′, N’Male’, CAST(59.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Finland’, N’2000′, N’Male’, CAST(65.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’France’, N’2000′, N’Male’, CAST(67.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Gabon’, N’2000′, N’Male’, CAST(51.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Gambia’, N’2000′, N’Male’, CAST(48.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Georgia’, N’2000′, N’Male’, CAST(61.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Germany’, N’2000′, N’Male’, CAST(66.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ghana’, N’2000′, N’Male’, CAST(49.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Greece’, N’2000′, N’Male’, CAST(67.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Grenada’, N’2000′, N’Male’, CAST(60.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guatemala’, N’2000′, N’Male’, CAST(53.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guinea’, N’2000′, N’Male’, CAST(45.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guinea-Bissau’, N’2000′, N’Male’, CAST(44.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guyana’, N’2000′, N’Male’, CAST(55.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Haiti’, N’2000′, N’Male’, CAST(49.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Honduras’, N’2000′, N’Male’, CAST(60.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Hungary’, N’2000′, N’Male’, CAST(60.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Iceland’, N’2000′, N’Male’, CAST(69.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’India’, N’2000′, N’Male’, CAST(53.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Indonesia’, N’2000′, N’Male’, CAST(58.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Iran (Islamic Republic of)’, N’2000′, N’Male’, CAST(61.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Iraq’, N’2000′, N’Male’, CAST(59.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ireland’, N’2000′, N’Male’, CAST(65.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Israel’, N’2000′, N’Male’, CAST(68.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Italy’, N’2000′, N’Male’, CAST(68.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Jamaica’, N’2000′, N’Male’, CAST(62.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Japan’, N’2000′, N’Male’, CAST(70.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Jordan’, N’2000′, N’Male’, CAST(62.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kazakhstan’, N’2000′, N’Male’, CAST(53.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kenya’, N’2000′, N’Male’, CAST(44.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kiribati’, N’2000′, N’Male’, CAST(54.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kuwait’, N’2000′, N’Male’, CAST(63.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kyrgyzstan’, N’2000′, N’Male’, CAST(56.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lao People”s Democratic Republic’, N’2000′, N’Male’, CAST(49.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Latvia’, N’2000′, N’Male’, CAST(58.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lebanon’, N’2000′, N’Male’, CAST(59.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lesotho’, N’2000′, N’Male’, CAST(42.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Liberia’, N’2000′, N’Male’, CAST(43.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Libya’, N’2000′, N’Male’, CAST(61.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lithuania’, N’2000′, N’Male’, CAST(59.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Luxembourg’, N’2000′, N’Male’, CAST(66.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Madagascar’, N’2000′, N’Male’, CAST(49.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Malawi’, N’2000′, N’Male’, CAST(36.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Malaysia’, N’2000′, N’Male’, CAST(62.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Maldives’, N’2000′, N’Male’, CAST(61.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mali’, N’2000′, N’Male’, CAST(43.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Malta’, N’2000′, N’Male’, CAST(67.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mauritania’, N’2000′, N’Male’, CAST(51.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mauritius’, N’2000′, N’Male’, CAST(61.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mexico’, N’2000′, N’Male’, CAST(64.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Micronesia (Federated States of)’, N’2000′, N’Male’, CAST(59.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mongolia’, N’2000′, N’Male’, CAST(54.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Montenegro’, N’2000′, N’Male’, CAST(62.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Morocco’, N’2000′, N’Male’, CAST(59.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mozambique’, N’2000′, N’Male’, CAST(41.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Myanmar’, N’2000′, N’Male’, CAST(53.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Namibia’, N’2000′, N’Male’, CAST(49.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Nepal’, N’2000′, N’Male’, CAST(54.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Netherlands’, N’2000′, N’Male’, CAST(67.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’New Zealand’, N’2000′, N’Male’, CAST(67.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Nicaragua’, N’2000′, N’Male’, CAST(48.60 AS Numeric(18, 2)))
GO
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Niger’, N’2000′, N’Male’, CAST(43.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Nigeria’, N’2000′, N’Male’, CAST(40.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Norway’, N’2000′, N’Male’, CAST(67.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Oman’, N’2000′, N’Male’, CAST(62.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Pakistan’, N’2000′, N’Male’, CAST(54.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Panama’, N’2000′, N’Male’, CAST(64.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Papua New Guinea’, N’2000′, N’Male’, CAST(51.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Paraguay’, N’2000′, N’Male’, CAST(61.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Peru’, N’2000′, N’Male’, CAST(57.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Philippines’, N’2000′, N’Male’, CAST(56.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Poland’, N’2000′, N’Male’, CAST(62.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Portugal’, N’2000′, N’Male’, CAST(65.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Qatar’, N’2000′, N’Male’, CAST(64.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Republic of Korea’, N’2000′, N’Male’, CAST(65.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Republic of Moldova’, N’2000′, N’Male’, CAST(57.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Romania’, N’2000′, N’Male’, CAST(60.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Russian Federation’, N’2000′, N’Male’, CAST(53.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Rwanda’, N’2000′, N’Male’, CAST(35.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Saint Lucia’, N’2000′, N’Male’, CAST(62.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Saint Vincent and the Grenadines’, N’2000′, N’Male’, CAST(61.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Samoa’, N’2000′, N’Male’, CAST(61.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sao Tome and Principe’, N’2000′, N’Male’, CAST(53.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Saudi Arabia’, N’2000′, N’Male’, CAST(61.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Senegal’, N’2000′, N’Male’, CAST(49.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Serbia’, N’2000′, N’Male’, CAST(62.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Seychelles’, N’2000′, N’Male’, CAST(61.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sierra Leone’, N’2000′, N’Male’, CAST(33.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Singapore’, N’2000′, N’Male’, CAST(68.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Slovakia’, N’2000′, N’Male’, CAST(61.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Slovenia’, N’2000′, N’Male’, CAST(63.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Solomon Islands’, N’2000′, N’Male’, CAST(58.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Somalia’, N’2000′, N’Male’, CAST(42.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’South Africa’, N’2000′, N’Male’, CAST(48.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’South Sudan’, N’2000′, N’Male’, CAST(41.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Spain’, N’2000′, N’Male’, CAST(66.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sri Lanka’, N’2000′, N’Male’, CAST(61.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sudan’, N’2000′, N’Male’, CAST(49.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Suriname’, N’2000′, N’Male’, CAST(57.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Swaziland’, N’2000′, N’Male’, CAST(41.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sweden’, N’2000′, N’Male’, CAST(68.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Switzerland’, N’2000′, N’Male’, CAST(67.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Syrian Arab Republic’, N’2000′, N’Male’, CAST(60.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Tajikistan’, N’2000′, N’Male’, CAST(53.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Thailand’, N’2000′, N’Male’, CAST(61.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Macedonia’, N’2000′, N’Male’, CAST(62.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Timor-Leste’, N’2000′, N’Male’, CAST(50.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Togo’, N’2000′, N’Male’, CAST(47.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Tonga’, N’2000′, N’Male’, CAST(63.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Trinidad and Tobago’, N’2000′, N’Male’, CAST(59.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Tunisia’, N’2000′, N’Male’, CAST(62.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Turkey’, N’2000′, N’Male’, CAST(59.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Turkmenistan’, N’2000′, N’Male’, CAST(54.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Uganda’, N’2000′, N’Male’, CAST(39.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ukraine’, N’2000′, N’Male’, CAST(56.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United Arab Emirates’, N’2000′, N’Male’, CAST(64.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United Kingdom’, N’2000′, N’Male’, CAST(67.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United Republic of Tanzania’, N’2000′, N’Male’, CAST(42.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United States of America’, N’2000′, N’Male’, CAST(65.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Uruguay’, N’2000′, N’Male’, CAST(63.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Uzbekistan’, N’2000′, N’Male’, CAST(57.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Vanuatu’, N’2000′, N’Male’, CAST(60.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Venezuela’, N’2000′, N’Male’, CAST(60.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Viet Nam’, N’2000′, N’Male’, CAST(59.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Yemen’, N’2000′, N’Male’, CAST(52.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Zambia’, N’2000′, N’Male’, CAST(36.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Zimbabwe’, N’2000′, N’Male’, CAST(39.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Afghanistan’, N’2015′, N’Female’, CAST(53.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Albania’, N’2015′, N’Female’, CAST(71.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Algeria’, N’2015′, N’Female’, CAST(67.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Angola’, N’2015′, N’Female’, CAST(47.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Antigua and Barbuda’, N’2015′, N’Female’, CAST(68.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Argentina’, N’2015′, N’Female’, CAST(70.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Armenia’, N’2015′, N’Female’, CAST(68.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Australia’, N’2015′, N’Female’, CAST(72.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Austria’, N’2015′, N’Female’, CAST(73.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Azerbaijan’, N’2015′, N’Female’, CAST(66.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bahamas’, N’2015′, N’Female’, CAST(68.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bahrain’, N’2015′, N’Female’, CAST(67.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bangladesh’, N’2015′, N’Female’, CAST(62.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Barbados’, N’2015′, N’Female’, CAST(68.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Belarus’, N’2015′, N’Female’, CAST(69.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Belgium’, N’2015′, N’Female’, CAST(72.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Belize’, N’2015′, N’Female’, CAST(63.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Benin’, N’2015′, N’Female’, CAST(52.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bhutan’, N’2015′, N’Female’, CAST(61.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bolivia (Plurinational State of)’, N’2015′, N’Female’, CAST(64.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bosnia and Herzegovina’, N’2015′, N’Female’, CAST(70.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Botswana’, N’2015′, N’Female’, CAST(58.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Brazil’, N’2015′, N’Female’, CAST(67.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Brunei Darussalam’, N’2015′, N’Female’, CAST(71.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bulgaria’, N’2015′, N’Female’, CAST(69.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Burkina Faso’, N’2015′, N’Female’, CAST(52.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Burundi’, N’2015′, N’Female’, CAST(53.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cote d”Ivoire’, N’2015′, N’Female’, CAST(47.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cabo Verde’, N’2015′, N’Female’, CAST(65.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cambodia’, N’2015′, N’Female’, CAST(60.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cameroon’, N’2015′, N’Female’, CAST(51.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Canada’, N’2015′, N’Female’, CAST(73.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Central African Republic’, N’2015′, N’Female’, CAST(47.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Chad’, N’2015′, N’Female’, CAST(47.10 AS Numeric(18, 2)))
GO
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Chile’, N’2015′, N’Female’, CAST(72.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’China’, N’2015′, N’Female’, CAST(69.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Colombia’, N’2015′, N’Female’, CAST(67.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Comoros’, N’2015′, N’Female’, CAST(57.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Congo’, N’2015′, N’Female’, CAST(57.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Costa Rica’, N’2015′, N’Female’, CAST(71.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Croatia’, N’2015′, N’Female’, CAST(71.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cuba’, N’2015′, N’Female’, CAST(70.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cyprus’, N’2015′, N’Female’, CAST(72.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Czech Republic’, N’2015′, N’Female’, CAST(71.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Democratic People”s Republic of Korea’, N’2015′, N’Female’, CAST(66.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Democratic Republic of the Congo’, N’2015′, N’Female’, CAST(53.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Denmark’, N’2015′, N’Female’, CAST(72.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Djibouti’, N’2015′, N’Female’, CAST(57.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Dominican Republic’, N’2015′, N’Female’, CAST(67.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ecuador’, N’2015′, N’Female’, CAST(68.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Egypt’, N’2015′, N’Female’, CAST(63.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’El Salvador’, N’2015′, N’Female’, CAST(67.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Equatorial Guinea’, N’2015′, N’Female’, CAST(52.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Eritrea’, N’2015′, N’Female’, CAST(57.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Estonia’, N’2015′, N’Female’, CAST(72.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ethiopia’, N’2015′, N’Female’, CAST(57.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Fiji’, N’2015′, N’Female’, CAST(65.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Finland’, N’2015′, N’Female’, CAST(72.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’France’, N’2015′, N’Female’, CAST(74.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Gabon’, N’2015′, N’Female’, CAST(58.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Gambia’, N’2015′, N’Female’, CAST(54.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Georgia’, N’2015′, N’Female’, CAST(69.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Germany’, N’2015′, N’Female’, CAST(72.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ghana’, N’2015′, N’Female’, CAST(56.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Greece’, N’2015′, N’Female’, CAST(73.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Grenada’, N’2015′, N’Female’, CAST(66.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guatemala’, N’2015′, N’Female’, CAST(64.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guinea’, N’2015′, N’Female’, CAST(51.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guinea-Bissau’, N’2015′, N’Female’, CAST(52.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guyana’, N’2015′, N’Female’, CAST(60.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Haiti’, N’2015′, N’Female’, CAST(56.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Honduras’, N’2015′, N’Female’, CAST(66.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Hungary’, N’2015′, N’Female’, CAST(69.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Iceland’, N’2015′, N’Female’, CAST(73.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’India’, N’2015′, N’Female’, CAST(60.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Indonesia’, N’2015′, N’Female’, CAST(63.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Iran (Islamic Republic of)’, N’2015′, N’Female’, CAST(66.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Iraq’, N’2015′, N’Female’, CAST(62.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ireland’, N’2015′, N’Female’, CAST(72.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Israel’, N’2015′, N’Female’, CAST(73.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Italy’, N’2015′, N’Female’, CAST(73.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Jamaica’, N’2015′, N’Female’, CAST(68.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Japan’, N’2015′, N’Female’, CAST(77.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Jordan’, N’2015′, N’Female’, CAST(65.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kazakhstan’, N’2015′, N’Female’, CAST(66.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kenya’, N’2015′, N’Female’, CAST(57.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kiribati’, N’2015′, N’Female’, CAST(60.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kuwait’, N’2015′, N’Female’, CAST(66.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kyrgyzstan’, N’2015′, N’Female’, CAST(66.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lao People”s Democratic Republic’, N’2015′, N’Female’, CAST(59.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Latvia’, N’2015′, N’Female’, CAST(70.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lebanon’, N’2015′, N’Female’, CAST(66.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lesotho’, N’2015′, N’Female’, CAST(47.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Liberia’, N’2015′, N’Female’, CAST(53.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Libya’, N’2015′, N’Female’, CAST(65.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lithuania’, N’2015′, N’Female’, CAST(70.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Luxembourg’, N’2015′, N’Female’, CAST(73.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Madagascar’, N’2015′, N’Female’, CAST(58.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Malawi’, N’2015′, N’Female’, CAST(52.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Malaysia’, N’2015′, N’Female’, CAST(68.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Maldives’, N’2015′, N’Female’, CAST(70.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mali’, N’2015′, N’Female’, CAST(50.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Malta’, N’2015′, N’Female’, CAST(72.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mauritania’, N’2015′, N’Female’, CAST(55.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mauritius’, N’2015′, N’Female’, CAST(69.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mexico’, N’2015′, N’Female’, CAST(69.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Micronesia (Federated States of)’, N’2015′, N’Female’, CAST(63.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mongolia’, N’2015′, N’Female’, CAST(65.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Montenegro’, N’2015′, N’Female’, CAST(69.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Morocco’, N’2015′, N’Female’, CAST(65.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mozambique’, N’2015′, N’Female’, CAST(50.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Myanmar’, N’2015′, N’Female’, CAST(60.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Namibia’, N’2015′, N’Female’, CAST(59.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Nepal’, N’2015′, N’Female’, CAST(62.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Netherlands’, N’2015′, N’Female’, CAST(73.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’New Zealand’, N’2015′, N’Female’, CAST(72.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Nicaragua’, N’2015′, N’Female’, CAST(67.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Niger’, N’2015′, N’Female’, CAST(54.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Nigeria’, N’2015′, N’Female’, CAST(48.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Norway’, N’2015′, N’Female’, CAST(73.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Oman’, N’2015′, N’Female’, CAST(67.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Pakistan’, N’2015′, N’Female’, CAST(58.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Panama’, N’2015′, N’Female’, CAST(70.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Papua New Guinea’, N’2015′, N’Female’, CAST(58.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Paraguay’, N’2015′, N’Female’, CAST(66.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Peru’, N’2015′, N’Female’, CAST(67.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Philippines’, N’2015′, N’Female’, CAST(63.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Poland’, N’2015′, N’Female’, CAST(71.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Portugal’, N’2015′, N’Female’, CAST(73.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Qatar’, N’2015′, N’Female’, CAST(68.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Republic of Korea’, N’2015′, N’Female’, CAST(75.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Republic of Moldova’, N’2015′, N’Female’, CAST(67.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Romania’, N’2015′, N’Female’, CAST(69.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Russian Federation’, N’2015′, N’Female’, CAST(67.80 AS Numeric(18, 2)))
GO
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Rwanda’, N’2015′, N’Female’, CAST(60.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Saint Lucia’, N’2015′, N’Female’, CAST(67.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Saint Vincent and the Grenadines’, N’2015′, N’Female’, CAST(65.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Samoa’, N’2015′, N’Female’, CAST(69.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sao Tome and Principe’, N’2015′, N’Female’, CAST(60.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Saudi Arabia’, N’2015′, N’Female’, CAST(64.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Senegal’, N’2015′, N’Female’, CAST(59.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Serbia’, N’2015′, N’Female’, CAST(69.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Seychelles’, N’2015′, N’Female’, CAST(69.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sierra Leone’, N’2015′, N’Female’, CAST(44.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Singapore’, N’2015′, N’Female’, CAST(75.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Slovakia’, N’2015′, N’Female’, CAST(70.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Slovenia’, N’2015′, N’Female’, CAST(73.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Solomon Islands’, N’2015′, N’Female’, CAST(62.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Somalia’, N’2015′, N’Female’, CAST(48.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’South Africa’, N’2015′, N’Female’, CAST(56.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’South Sudan’, N’2015′, N’Female’, CAST(50.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Spain’, N’2015′, N’Female’, CAST(74.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sri Lanka’, N’2015′, N’Female’, CAST(69.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sudan’, N’2015′, N’Female’, CAST(56.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Suriname’, N’2015′, N’Female’, CAST(64.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Swaziland’, N’2015′, N’Female’, CAST(52.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sweden’, N’2015′, N’Female’, CAST(73.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Switzerland’, N’2015′, N’Female’, CAST(74.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Syrian Arab Republic’, N’2015′, N’Female’, CAST(59.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Tajikistan’, N’2015′, N’Female’, CAST(65.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Thailand’, N’2015′, N’Female’, CAST(68.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Macedonia’, N’2015′, N’Female’, CAST(69.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Timor-Leste’, N’2015′, N’Female’, CAST(62.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Togo’, N’2015′, N’Female’, CAST(53.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Tonga’, N’2015′, N’Female’, CAST(67.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Trinidad and Tobago’, N’2015′, N’Female’, CAST(65.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Tunisia’, N’2015′, N’Female’, CAST(68.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Turkey’, N’2015′, N’Female’, CAST(67.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Turkmenistan’, N’2015′, N’Female’, CAST(63.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Uganda’, N’2015′, N’Female’, CAST(55.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ukraine’, N’2015′, N’Female’, CAST(67.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United Arab Emirates’, N’2015′, N’Female’, CAST(68.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United Kingdom ‘, N’2015′, N’Female’, CAST(72.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United Republic of Tanzania’, N’2015′, N’Female’, CAST(55.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United States of America’, N’2015′, N’Female’, CAST(70.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Uruguay’, N’2015′, N’Female’, CAST(70.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Uzbekistan’, N’2015′, N’Female’, CAST(65.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Vanuatu’, N’2015′, N’Female’, CAST(66.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Venezuela’, N’2015′, N’Female’, CAST(68.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Viet Nam’, N’2015′, N’Female’, CAST(69.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Yemen’, N’2015′, N’Female’, CAST(58.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Zambia’, N’2015′, N’Female’, CAST(55.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Zimbabwe’, N’2015′, N’Female’, CAST(53.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Afghanistan’, N’2000′, N’Female’, CAST(48.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Albania’, N’2000′, N’Female’, CAST(66.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Algeria’, N’2000′, N’Female’, CAST(63.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Angola’, N’2000′, N’Female’, CAST(40.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Antigua and Barbuda’, N’2000′, N’Female’, CAST(66.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Argentina’, N’2000′, N’Female’, CAST(67.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Armenia’, N’2000′, N’Female’, CAST(66.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Australia’, N’2000′, N’Female’, CAST(71.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Austria’, N’2000′, N’Female’, CAST(70.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Azerbaijan’, N’2000′, N’Female’, CAST(61.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bahamas’, N’2000′, N’Female’, CAST(65.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bahrain’, N’2000′, N’Female’, CAST(65.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bangladesh’, N’2000′, N’Female’, CAST(56.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Barbados’, N’2000′, N’Female’, CAST(66.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Belarus’, N’2000′, N’Female’, CAST(65.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Belgium’, N’2000′, N’Female’, CAST(70.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Belize’, N’2000′, N’Female’, CAST(62.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Benin’, N’2000′, N’Female’, CAST(48.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bhutan’, N’2000′, N’Female’, CAST(52.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bolivia (Plurinational State of)’, N’2000′, N’Female’, CAST(56.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bosnia and Herzegovina’, N’2000′, N’Female’, CAST(67.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Botswana’, N’2000′, N’Female’, CAST(41.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Brazil’, N’2000′, N’Female’, CAST(64.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Brunei Darussalam’, N’2000′, N’Female’, CAST(68.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bulgaria’, N’2000′, N’Female’, CAST(66.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Burkina Faso’, N’2000′, N’Female’, CAST(44.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Burundi’, N’2000′, N’Female’, CAST(45.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cote d”Ivoire’, N’2000′, N’Female’, CAST(42.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cabo Verde’, N’2000′, N’Female’, CAST(61.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cambodia’, N’2000′, N’Female’, CAST(48.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cameroon’, N’2000′, N’Female’, CAST(45.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Canada’, N’2000′, N’Female’, CAST(71.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Central African Republic’, N’2000′, N’Female’, CAST(40.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Chad’, N’2000′, N’Female’, CAST(42.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Chile’, N’2000′, N’Female’, CAST(69.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’China’, N’2000′, N’Female’, CAST(65.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Colombia’, N’2000′, N’Female’, CAST(65.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Comoros’, N’2000′, N’Female’, CAST(53.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Congo’, N’2000′, N’Female’, CAST(46.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Costa Rica’, N’2000′, N’Female’, CAST(69.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Croatia’, N’2000′, N’Female’, CAST(69.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cuba’, N’2000′, N’Female’, CAST(68.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cyprus’, N’2000′, N’Female’, CAST(70.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Czech Republic’, N’2000′, N’Female’, CAST(68.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Democratic People”s Republic of Korea’, N’2000′, N’Female’, CAST(61.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Democratic Republic of the Congo’, N’2000′, N’Female’, CAST(45.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Denmark’, N’2000′, N’Female’, CAST(69.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Djibouti’, N’2000′, N’Female’, CAST(51.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Dominican Republic’, N’2000′, N’Female’, CAST(64.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ecuador’, N’2000′, N’Female’, CAST(65.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Egypt’, N’2000′, N’Female’, CAST(61.10 AS Numeric(18, 2)))
GO
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’El Salvador’, N’2000′, N’Female’, CAST(63.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Equatorial Guinea’, N’2000′, N’Female’, CAST(47.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Eritrea’, N’2000′, N’Female’, CAST(44.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Estonia’, N’2000′, N’Female’, CAST(67.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ethiopia’, N’2000′, N’Female’, CAST(46.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Fiji’, N’2000′, N’Female’, CAST(62.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Finland’, N’2000′, N’Female’, CAST(70.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’France’, N’2000′, N’Female’, CAST(72.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Gabon’, N’2000′, N’Female’, CAST(52.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Gambia’, N’2000′, N’Female’, CAST(49.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Georgia’, N’2000′, N’Female’, CAST(66.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Germany’, N’2000′, N’Female’, CAST(70.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ghana’, N’2000′, N’Female’, CAST(51.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Greece’, N’2000′, N’Female’, CAST(71.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Grenada’, N’2000′, N’Female’, CAST(63.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guatemala’, N’2000′, N’Female’, CAST(60.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guinea’, N’2000′, N’Female’, CAST(46.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guinea-Bissau’, N’2000′, N’Female’, CAST(46.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guyana’, N’2000′, N’Female’, CAST(59.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Haiti’, N’2000′, N’Female’, CAST(52.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Honduras’, N’2000′, N’Female’, CAST(62.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Hungary’, N’2000′, N’Female’, CAST(66.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Iceland’, N’2000′, N’Female’, CAST(71.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’India’, N’2000′, N’Female’, CAST(54.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Indonesia’, N’2000′, N’Female’, CAST(60.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Iran (Islamic Republic of)’, N’2000′, N’Female’, CAST(62.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Iraq’, N’2000′, N’Female’, CAST(62.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ireland’, N’2000′, N’Female’, CAST(69.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Israel’, N’2000′, N’Female’, CAST(70.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Italy’, N’2000′, N’Female’, CAST(71.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Jamaica’, N’2000′, N’Female’, CAST(65.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Japan’, N’2000′, N’Female’, CAST(75.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Jordan’, N’2000′, N’Female’, CAST(63.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kazakhstan’, N’2000′, N’Female’, CAST(62.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kenya’, N’2000′, N’Female’, CAST(46.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kiribati’, N’2000′, N’Female’, CAST(58.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kuwait’, N’2000′, N’Female’, CAST(65.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kyrgyzstan’, N’2000′, N’Female’, CAST(63.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lao People”s Democratic Republic’, N’2000′, N’Female’, CAST(52.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Latvia’, N’2000′, N’Female’, CAST(67.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lebanon’, N’2000′, N’Female’, CAST(62.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lesotho’, N’2000′, N’Female’, CAST(43.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Liberia’, N’2000′, N’Female’, CAST(44.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Libya’, N’2000′, N’Female’, CAST(63.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lithuania’, N’2000′, N’Female’, CAST(68.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Luxembourg’, N’2000′, N’Female’, CAST(70.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Madagascar’, N’2000′, N’Female’, CAST(51.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Malawi’, N’2000′, N’Female’, CAST(37.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Malaysia’, N’2000′, N’Female’, CAST(66.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Maldives’, N’2000′, N’Female’, CAST(62.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mali’, N’2000′, N’Female’, CAST(43.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Malta’, N’2000′, N’Female’, CAST(69.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mauritania’, N’2000′, N’Female’, CAST(52.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mauritius’, N’2000′, N’Female’, CAST(66.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mexico’, N’2000′, N’Female’, CAST(67.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Micronesia (Federated States of)’, N’2000′, N’Female’, CAST(60.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mongolia’, N’2000′, N’Female’, CAST(59.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Montenegro’, N’2000′, N’Female’, CAST(66.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Morocco’, N’2000′, N’Female’, CAST(61.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mozambique’, N’2000′, N’Female’, CAST(43.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Myanmar’, N’2000′, N’Female’, CAST(56.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Namibia’, N’2000′, N’Female’, CAST(50.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Nepal’, N’2000′, N’Female’, CAST(55.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Netherlands’, N’2000′, N’Female’, CAST(70.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’New Zealand’, N’2000′, N’Female’, CAST(70.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Nicaragua’, N’2000′, N’Female’, CAST(60.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Niger’, N’2000′, N’Female’, CAST(43.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Nigeria’, N’2000′, N’Female’, CAST(41.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Norway’, N’2000′, N’Female’, CAST(71.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Oman’, N’2000′, N’Female’, CAST(64.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Pakistan’, N’2000′, N’Female’, CAST(54.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Panama’, N’2000′, N’Female’, CAST(68.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Papua New Guinea’, N’2000′, N’Female’, CAST(54.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Paraguay’, N’2000′, N’Female’, CAST(63.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Peru’, N’2000′, N’Female’, CAST(62.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Philippines’, N’2000′, N’Female’, CAST(61.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Poland’, N’2000′, N’Female’, CAST(68.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Portugal’, N’2000′, N’Female’, CAST(70.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Qatar’, N’2000′, N’Female’, CAST(66.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Republic of Korea’, N’2000′, N’Female’, CAST(70.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Republic of Moldova’, N’2000′, N’Female’, CAST(63.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Romania’, N’2000′, N’Female’, CAST(66.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Russian Federation’, N’2000′, N’Female’, CAST(63.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Rwanda’, N’2000′, N’Female’, CAST(44.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Saint Lucia’, N’2000′, N’Female’, CAST(64.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Saint Vincent and the Grenadines’, N’2000′, N’Female’, CAST(63.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Samoa’, N’2000′, N’Female’, CAST(66.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sao Tome and Principe’, N’2000′, N’Female’, CAST(56.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Saudi Arabia’, N’2000′, N’Female’, CAST(64.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Senegal’, N’2000′, N’Female’, CAST(51.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Serbia’, N’2000′, N’Female’, CAST(66.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Seychelles’, N’2000′, N’Female’, CAST(68.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sierra Leone’, N’2000′, N’Female’, CAST(34.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Singapore’, N’2000′, N’Female’, CAST(71.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Slovakia’, N’2000′, N’Female’, CAST(68.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Slovenia’, N’2000′, N’Female’, CAST(69.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Solomon Islands’, N’2000′, N’Female’, CAST(59.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Somalia’, N’2000′, N’Female’, CAST(44.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’South Africa’, N’2000′, N’Female’, CAST(52.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’South Sudan’, N’2000′, N’Female’, CAST(43.20 AS Numeric(18, 2)))
GO
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Spain’, N’2000′, N’Female’, CAST(71.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sri Lanka’, N’2000′, N’Female’, CAST(66.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sudan’, N’2000′, N’Female’, CAST(52.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Suriname’, N’2000′, N’Female’, CAST(61.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Swaziland’, N’2000′, N’Female’, CAST(42.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sweden’, N’2000′, N’Female’, CAST(71.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Switzerland’, N’2000′, N’Female’, CAST(71.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Syrian Arab Republic’, N’2000′, N’Female’, CAST(63.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Tajikistan’, N’2000′, N’Female’, CAST(59.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Thailand’, N’2000′, N’Female’, CAST(65.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Macedonia’, N’2000′, N’Female’, CAST(66.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Timor-Leste’, N’2000′, N’Female’, CAST(53.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Togo’, N’2000′, N’Female’, CAST(48.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Tonga’, N’2000′, N’Female’, CAST(65.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Trinidad and Tobago’, N’2000′, N’Female’, CAST(64.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Tunisia’, N’2000′, N’Female’, CAST(66.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Turkey’, N’2000′, N’Female’, CAST(63.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Turkmenistan’, N’2000′, N’Female’, CAST(60.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Uganda’, N’2000′, N’Female’, CAST(40.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ukraine’, N’2000′, N’Female’, CAST(65.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United Arab Emirates’, N’2000′, N’Female’, CAST(66.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United Kingdom’, N’2000′, N’Female’, CAST(70.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United Republic of Tanzania’, N’2000′, N’Female’, CAST(43.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United States of America’, N’2000′, N’Female’, CAST(68.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Uruguay’, N’2000′, N’Female’, CAST(68.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Uzbekistan’, N’2000′, N’Female’, CAST(62.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Vanuatu’, N’2000′, N’Female’, CAST(63.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Venezuela’, N’2000′, N’Female’, CAST(67.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Viet Nam’, N’2000′, N’Female’, CAST(65.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Yemen’, N’2000′, N’Female’, CAST(53.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Zambia’, N’2000′, N’Female’, CAST(39.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Zimbabwe’, N’2000′, N’Female’, CAST(39.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Afghanistan’, N’2015′, N’Male’, CAST(51.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Albania’, N’2015′, N’Male’, CAST(66.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Algeria’, N’2015′, N’Male’, CAST(65.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Angola’, N’2015′, N’Male’, CAST(44.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Antigua and Barbuda’, N’2015′, N’Male’, CAST(66.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Argentina’, N’2015′, N’Male’, CAST(65.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Armenia’, N’2015′, N’Male’, CAST(64.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Australia’, N’2015′, N’Male’, CAST(70.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Austria’, N’2015′, N’Male’, CAST(70.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Azerbaijan’, N’2015′, N’Male’, CAST(62.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bahamas’, N’2015′, N’Male’, CAST(64.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bahrain’, N’2015′, N’Male’, CAST(66.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bangladesh’, N’2015′, N’Male’, CAST(61.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Barbados’, N’2015′, N’Male’, CAST(65.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Belarus’, N’2015′, N’Male’, CAST(60.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Belgium’, N’2015′, N’Male’, CAST(69.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Belize’, N’2015′, N’Male’, CAST(60.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Benin’, N’2015′, N’Male’, CAST(52.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bhutan’, N’2015′, N’Male’, CAST(61.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bolivia (Plurinational State of)’, N’2015′, N’Male’, CAST(61.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bosnia and Herzegovina’, N’2015′, N’Male’, CAST(66.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Botswana’, N’2015′, N’Male’, CAST(55.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Brazil’, N’2015′, N’Male’, CAST(63.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Brunei Darussalam’, N’2015′, N’Male’, CAST(69.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Bulgaria’, N’2015′, N’Male’, CAST(63.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Burkina Faso’, N’2015′, N’Male’, CAST(52.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Burundi’, N’2015′, N’Male’, CAST(50.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cote d”Ivoire’, N’2015′, N’Male’, CAST(46.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cabo Verde’, N’2015′, N’Male’, CAST(63.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cambodia’, N’2015′, N’Male’, CAST(55.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cameroon’, N’2015′, N’Male’, CAST(49.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Canada’, N’2015′, N’Male’, CAST(71.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Central African Republic’, N’2015′, N’Male’, CAST(44.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Chad’, N’2015′, N’Male’, CAST(45.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Chile’, N’2015′, N’Male’, CAST(68.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’China’, N’2015′, N’Male’, CAST(67.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Colombia’, N’2015′, N’Male’, CAST(62.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Comoros’, N’2015′, N’Male’, CAST(54.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Congo’, N’2015′, N’Male’, CAST(55.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Costa Rica’, N’2015′, N’Male’, CAST(68.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Croatia’, N’2015′, N’Male’, CAST(67.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cuba’, N’2015′, N’Male’, CAST(68.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Cyprus’, N’2015′, N’Male’, CAST(70.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Czech Republic’, N’2015′, N’Male’, CAST(67.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Democratic People”s Republic of Korea’, N’2015′, N’Male’, CAST(61.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Democratic Republic of the Congo’, N’2015′, N’Male’, CAST(50.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Denmark’, N’2015′, N’Male’, CAST(70.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Djibouti’, N’2015′, N’Male’, CAST(54.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Dominican Republic’, N’2015′, N’Male’, CAST(63.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ecuador’, N’2015′, N’Male’, CAST(65.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Egypt’, N’2015′, N’Male’, CAST(61.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’El Salvador’, N’2015′, N’Male’, CAST(60.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Equatorial Guinea’, N’2015′, N’Male’, CAST(50.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Eritrea’, N’2015′, N’Male’, CAST(54.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Estonia’, N’2015′, N’Male’, CAST(65.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ethiopia’, N’2015′, N’Male’, CAST(54.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Fiji’, N’2015′, N’Male’, CAST(60.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Finland’, N’2015′, N’Male’, CAST(69.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’France’, N’2015′, N’Male’, CAST(70.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Gabon’, N’2015′, N’Male’, CAST(56.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Gambia’, N’2015′, N’Male’, CAST(53.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Georgia’, N’2015′, N’Male’, CAST(63.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Germany’, N’2015′, N’Male’, CAST(69.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ghana’, N’2015′, N’Male’, CAST(54.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Greece’, N’2015′, N’Male’, CAST(70.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Grenada’, N’2015′, N’Male’, CAST(63.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guatemala’, N’2015′, N’Male’, CAST(59.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guinea’, N’2015′, N’Male’, CAST(51.50 AS Numeric(18, 2)))
GO
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guinea-Bissau’, N’2015′, N’Male’, CAST(50.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Guyana’, N’2015′, N’Male’, CAST(57.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Haiti’, N’2015′, N’Male’, CAST(54.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Honduras’, N’2015′, N’Male’, CAST(63.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Hungary’, N’2015′, N’Male’, CAST(64.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Iceland’, N’2015′, N’Male’, CAST(71.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’India’, N’2015′, N’Male’, CAST(58.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Indonesia’, N’2015′, N’Male’, CAST(60.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Iran (Islamic Republic of)’, N’2015′, N’Male’, CAST(66.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Iraq’, N’2015′, N’Male’, CAST(58.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ireland’, N’2015′, N’Male’, CAST(70.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Israel’, N’2015′, N’Male’, CAST(71.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Italy’, N’2015′, N’Male’, CAST(71.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Jamaica’, N’2015′, N’Male’, CAST(65.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Japan’, N’2015′, N’Male’, CAST(72.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Jordan’, N’2015′, N’Male’, CAST(64.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kazakhstan’, N’2015′, N’Male’, CAST(59.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kenya’, N’2015′, N’Male’, CAST(53.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kiribati’, N’2015′, N’Male’, CAST(56.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kuwait’, N’2015′, N’Male’, CAST(65.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Kyrgyzstan’, N’2015′, N’Male’, CAST(61.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lao People”s Democratic Republic’, N’2015′, N’Male’, CAST(56.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Latvia’, N’2015′, N’Male’, CAST(63.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lebanon’, N’2015′, N’Male’, CAST(64.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lesotho’, N’2015′, N’Male’, CAST(45.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Liberia’, N’2015′, N’Male’, CAST(51.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Libya’, N’2015′, N’Male’, CAST(62.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Lithuania’, N’2015′, N’Male’, CAST(62.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Luxembourg’, N’2015′, N’Male’, CAST(70.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Madagascar’, N’2015′, N’Male’, CAST(55.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Malawi’, N’2015′, N’Male’, CAST(50.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Malaysia’, N’2015′, N’Male’, CAST(64.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Maldives’, N’2015′, N’Male’, CAST(68.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mali’, N’2015′, N’Male’, CAST(51.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Malta’, N’2015′, N’Male’, CAST(70.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mauritania’, N’2015′, N’Male’, CAST(54.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mauritius’, N’2015′, N’Male’, CAST(64.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mexico’, N’2015′, N’Male’, CAST(65.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Micronesia (Federated States of)’, N’2015′, N’Male’, CAST(61.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mongolia’, N’2015′, N’Male’, CAST(58.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Montenegro’, N’2015′, N’Male’, CAST(66.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Morocco’, N’2015′, N’Male’, CAST(64.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Mozambique’, N’2015′, N’Male’, CAST(48.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Myanmar’, N’2015′, N’Male’, CAST(57.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Namibia’, N’2015′, N’Male’, CAST(55.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Nepal’, N’2015′, N’Male’, CAST(60.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Netherlands’, N’2015′, N’Male’, CAST(71.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’New Zealand’, N’2015′, N’Male’, CAST(70.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Nicaragua’, N’2015′, N’Male’, CAST(60.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Niger’, N’2015′, N’Male’, CAST(53.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Nigeria’, N’2015′, N’Male’, CAST(46.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Norway’, N’2015′, N’Male’, CAST(70.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Oman’, N’2015′, N’Male’, CAST(65.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Pakistan’, N’2015′, N’Male’, CAST(57.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Panama’, N’2015′, N’Male’, CAST(66.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Papua New Guinea’, N’2015′, N’Male’, CAST(54.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Paraguay’, N’2015′, N’Male’, CAST(64.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Peru’, N’2015′, N’Male’, CAST(63.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Philippines’, N’2015′, N’Male’, CAST(58.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Poland’, N’2015′, N’Male’, CAST(65.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Portugal’, N’2015′, N’Male’, CAST(69.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Qatar’, N’2015′, N’Male’, CAST(67.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Republic of Korea’, N’2015′, N’Male’, CAST(70.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Republic of Moldova’, N’2015′, N’Male’, CAST(61.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Romania’, N’2015′, N’Male’, CAST(64.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Russian Federation’, N’2015′, N’Male’, CAST(59.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Rwanda’, N’2015′, N’Male’, CAST(52.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Saint Lucia’, N’2015′, N’Male’, CAST(64.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Saint Vincent and the Grenadines’, N’2015′, N’Male’, CAST(63.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Samoa’, N’2015′, N’Male’, CAST(64.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sao Tome and Principe’, N’2015′, N’Male’, CAST(57.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Saudi Arabia’, N’2015′, N’Male’, CAST(64.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Senegal’, N’2015′, N’Male’, CAST(57.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Serbia’, N’2015′, N’Male’, CAST(65.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Seychelles’, N’2015′, N’Male’, CAST(62.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sierra Leone’, N’2015′, N’Male’, CAST(43.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Singapore’, N’2015′, N’Male’, CAST(71.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Slovakia’, N’2015′, N’Male’, CAST(65.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Slovenia’, N’2015′, N’Male’, CAST(68.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Solomon Islands’, N’2015′, N’Male’, CAST(61.50 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Somalia’, N’2015′, N’Male’, CAST(46.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’South Africa’, N’2015′, N’Male’, CAST(51.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’South Sudan’, N’2015′, N’Male’, CAST(49.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Spain’, N’2015′, N’Male’, CAST(70.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sri Lanka’, N’2015′, N’Male’, CAST(64.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sudan’, N’2015′, N’Male’, CAST(54.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Suriname’, N’2015′, N’Male’, CAST(61.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Swaziland’, N’2015′, N’Male’, CAST(49.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Sweden’, N’2015′, N’Male’, CAST(71.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Switzerland’, N’2015′, N’Male’, CAST(71.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Syrian Arab Republic’, N’2015′, N’Male’, CAST(52.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Tajikistan’, N’2015′, N’Male’, CAST(59.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Thailand’, N’2015′, N’Male’, CAST(64.80 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Macedonia’, N’2015′, N’Male’, CAST(65.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Timor-Leste’, N’2015′, N’Male’, CAST(59.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Togo’, N’2015′, N’Male’, CAST(52.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Tonga’, N’2015′, N’Male’, CAST(64.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Trinidad and Tobago’, N’2015′, N’Male’, CAST(61.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Tunisia’, N’2015′, N’Male’, CAST(65.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Turkey’, N’2015′, N’Male’, CAST(64.50 AS Numeric(18, 2)))
GO
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Turkmenistan’, N’2015′, N’Male’, CAST(56.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Uganda’, N’2015′, N’Male’, CAST(53.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Ukraine’, N’2015′, N’Male’, CAST(60.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United Arab Emirates’, N’2015′, N’Male’, CAST(68.00 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United Kingdom’, N’2015′, N’Male’, CAST(70.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United Republic of Tanzania’, N’2015′, N’Male’, CAST(52.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’United States of America’, N’2015′, N’Male’, CAST(67.70 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Uruguay’, N’2015′, N’Male’, CAST(65.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Uzbekistan’, N’2015′, N’Male’, CAST(59.90 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Vanuatu’, N’2015′, N’Male’, CAST(63.40 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Venezuela’, N’2015′, N’Male’, CAST(62.30 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Viet Nam’, N’2015′, N’Male’, CAST(63.20 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Yemen’, N’2015′, N’Male’, CAST(57.10 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Zambia’, N’2015′, N’Male’, CAST(51.60 AS Numeric(18, 2)))
INSERT [dbo].[WHO_LifeExpectancy] ([Country], [Year], [Gender], [Age]) VALUES (N’Zimbabwe’, N’2015′, N’Male’, CAST(50.90 AS Numeric(18, 2)))