STATS 382 Alabama A & M University R coding Programming Task

The dataset PISAScores.csv contains three variables, Expenditure : per student spending in the country in

thousands, PISAReading: average score for the country for the reading portion of the standardized PISA

test, and TertiaryEd: the percentage of the country that completed a degree at a university or similar

learning institution. The project will focus mostly on the relationship between Expenditure and

PISAReading. Please do the following

1. Import the data.

2. Between Expenditure and PISAReading, determine which you believe is the explanatory variable

and which you believe is the response variable.

3. Explain what sort of association you expect between Expenditure and PISAReading and why.

4. Create a scatterplot of Expenditure and PISAReading.

5. Compute the value of the correlation coefficient and explain what information you get from the

value you computed.

6. Find the equation of the regression line for Expenditure and PISAReading.

7. Explain what the two parameters found for the regression line represent.

8. Add a graph of the regression line to the scatterplot for Expenditure and PISAReading.

9. Create a data frame consisting of the residuals for the regression line for Expenditure and

PISAReading

10. Make a scatterplot of the residuals and determine if it is consistent with what we would expect

if the regression line were appropriate.

11. Make a histogram of the residuals and determine if it is consistent with what we would expect if

the regression line were appropriate.

12. Use the Shapiro-Wilk test on the residuals and determine if it is consistent with what we would

expect if the regression line were appropriate.

13. Make a qq plot of the residuals and determine if it is consistent with what we would expect if

the regression line were appropriate.

14. Computer R2 and explain what information we get from it.

15. Create a matrix of scatterplots comparing each of the 3 variables to each other.

16. Compute a multilinear regression model using the 3 variables. Explain which variable you

believe is the predicted variable and which two you believe are predictors.

17. Using any method you like, determine if you think the addition of TertiaryEd made a better

model.