Regression Analysis and Credit Balance

Generate a scatterplot for CREDIT BALANCE vs. SIZE, including the graph of the “best fit” line. Interpret. Determine the equation of the “best fit” line, which describes the relationship between CREDIT BALANCE and SIZE 2591+ 403. 221 Determine the coefficient of correlation. Interpret. .75/ r-sq(56. 6%). There is a mild correlation. Determine the coefficient of determination. Interpret. 56. 6% Test the utility of this regression model (use a two tail test with ? =. 05). Interpret your results, including the p-value. P-value=0. Reject the null hpothesis.
T value 7. 9147 Based on your findings in 1-5, what is your opinion about using SIZE to predict CREDIT BALANCE? Size is a good predictor for credit balance. Compute the 95% confidence interval for beta-1 (the population slope). Interpret this interval. (300. 79, 505. 66) Using an interval, estimate the average credit balance for customers that have household size of 5. Interpret this interval. (4368. 20, 4846. 90) Using an interval, predict the credit balance for a customer that has a household size of 5. Interpret this interval. (3337. 87, 5877. 23)
What can we say about the credit balance for a customer that has a household size of 10? Denotes a point that is an extreme outlier in the predictors. Using MINITAB run the multiple regression analysis using the variables INCOME, SIZE and YEARS to predict CREDIT BALANCE. State the equation for this multiple regression model. Credit balance= 1276. 02+ 32. 2719 income(1000) + 346. 852 size + 7. 88209 years. Is this multiple regression model better than the linear model that we generated in parts 1-10? Yes. Reject years there is no corralation of coefficient. Income is useful and years in combination with size and income.