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17. Question 17: The following regression output estimates the impact of study time on grade point average. SUMMARY OUTPUT Regression Statistics Standard Error 2.756653112 Observations 9 ANOVA of SS Regression 249.0282676 Residual 53.19395466 Total co 302.2222222 Coefficients Standard Error Intercept 2.297229219 1.843257649 Study time 0.840050378 0.146744937 You are asked to do the following: (a) (5 pt) Compute the adjusted R2 for the regression (b) (2 pt) Compute a t statistic to test whether the impact of study time on grade point average is always positive, use the 99% confidence level. 3 (c) (2 pts) State the hypothesis for the F statistic to test the significance of the regression. Compute the F statistic and reach a decision, use the 95% confidence level.19. Question 19: The following regression output is the result of a multiple regression application in which we are interested in explaining the variation in retail price of personal computers based on three independent variables, CPU speed, RAM, and hard drive capacity. However, some of the regression output has been omitted. (a) (2 pts) Compute the t-statistic and test the null hypothesis that Process Mz is not significantly different from zero. Please explain your decision rule. 4 SUMMARY OUTPUT Regression Statistics Multiple R 0.834308875 R Square Adjusted R Square Standard Error Observations 36 ANOVA off SS Regression 34335282.67 Residual Total 49327249.56 Coefficients Standard Error Stat Intercept 45.95413592 730.8679496 Processor Mz 0. 193481924 2.557161 186 RAM 4.521583654 2.94317936 Hard Drive Capacity 174.042249 44.08895333 (b) (2 pts) Compute the t-statistic and test the null hypothesis that RAM is not significantly different from zero. Please explain your decision rule. (c) (6 pts) Given this information and your knowledge of multiple regression, what is the value for the standard error of the estimated regression