Do in SAS please

1. Create a permanent library called project using a LIBNAME statement. World Development Indicators Data Section

2. Read the WorldDevelopmentIndicators.xlsx file into your project library. Remember that you want to read in the Data sheet .

3. We can use PROC FREQ to help understand the dataset. Create one-way contingency tables for the Country_name and Indicator_name variables.

a) In a comment below your code, answer the following: How many observations are there for the Country_name European Union?

4. Next, well make a copy of the dataset with some changes.

a) Create another permanent dataset (dont overwrite the original data) that only includes observations where country_name is either Arab World, European Union, Latin America & Caribbean, or North America.

b) Rename the country_name variable to Region.

For the rest of this section, use the dataset youve created in question 4.

5. The data should already be sorted by country_name, however, use a PROC SORT step to sort your dataset by country_name just to be safe.

6. Use a PROC step to find (only) the mean, first quartile, median, third quartile, and standard deviation of the value variable for every country_name in your dataset.

a) In the PROC step where you do this, subset the data to only include observations where Indicator_name is "Urban population.

b) In a comment below your code, answer the following: Which region had the highest mean?

7- Use a PROC step to find the correlation between year and value.

a) In the PROC step where you do this, subset the data to only include

observations where Indicator_name is "Rural population.

8. (1pt) Repeat question 7 but find the correlations between year and value for each region. (This data should be subsetted as well.)

9. (4pts) Use a PROC step and a CLASS statement to find the mean, first quartile, median, third quartile, and standard deviation of the value variable for each Region and Year combination.

a) In the PROC step where you do this, subset the data to only include observations where Indicator_name is "Rural population.