1. Generate a scatter plot for CREDIT BALANCE vs. SIZE, including the graph of the "best fit" line. Interpret.
2. Determine the equation of the "best fit" line, which describes the relationship between CREDIT BALANCE and SIZE.
3. Determine the coefficient of correlation. Interpret.
4. Determine the coefficient of determination. Interpret.
5. Test the utility of this regression model (use a two tail test with ? =.05). Interpret your results, including the p-value.
6. Based on your findings in 1-5, what is your opinion about using SIZE to predict CREDIT BALANCE? Explain.
7. Compute the 95% confidence interval for beta-1 (the population slope). Interpret this interval.
8. Using an interval, estimate the average credit balance for customers that have household size of 5. Interpret this interval.
9. Using an interval, predict the credit balance for a customer that has a household size of 5. Interpret this interval.
10. What can we say about the credit balance for a customer that has a household size of 10? Explain your answer.
In an attempt to improve the model, we attempt to do a multiple regression model predicting CREDIT BALANCE based on INCOME, SIZE and YEARS.
11. Run the multiple regression analysis using the variables INCOME, SIZE and YEARS to predict CREDIT BALANCE. State the equation for this multiple regression model.
12. Perform the Global Test for Utility (F-Test). Explain your conclusion.
13. Perform the t-test on each independent variable. Explain your conclusions and clearly state how you should proceed. In particular, which independent variables should we keep and which should be discarded.
14. Is this multiple regression model better than the linear model that we generated in parts 1-10? Explain.