252x0772 11 26 07 Page layout view ECO252 QBA2 THIRD EXAM November 29 2007 Version 2 Name Student number Class Day and hour I 8 points Do all the following 2 points each unless noted otherwise Make Diagrams Show your work x N 27 14 1 P 46 x 77 2 P 21 x 39 3 P x 0 4 x 08 1 252x0772 11 26 07 Page layout view II 22 points Do all the following 2 points each unless noted otherwise Do not answer a question yes or no without giving reasons Show your work when appropriate Use a 5 significance level except where indicated otherwise Note that this is extremely long and that no one will do all the problems so look them over 1 Turn in your computer problems 2 and 3 marked as requested in the Take home 5 points 2 point penalty for not doing 2 In an ordinary 1 way ANOVA if the computed F statistic exceeds the value from the F table at the given significance level we can a Reject the null hypothesis because the difference between the means is not significant b Reject the null hypothesis because there is evidence of a significant difference between some of the means c Not reject the null hypothesis because the difference between the means is not significant d Not reject the null hypothesis because the difference between the means is significant c Not reject the null hypothesis because the difference between the variances is not significant d Not reject the null hypothesis because the difference between the variances is significant e None of the above 7 3 After an analysis if variance you would use the Tukey Kramer procedure or similar confidence intervals to check a For Normality b For equality of variances c For independence of error terms d For pairwise differences in means e For all of the above f For none of the above 4 If an ordinary one way ANOVA has 25 columns 17 rows and 17 25 425 the degrees of freedom for the F test are a 400 and 24 b 408 and 16 c 24 and 400 d 16 and 408 e 400 and 424 f 408 and 424 g 424 and 400 h 424 and 408 i 16 and 24 j None of the above The correct answer is 5 Assuming that your answer to 4 is correct and that the significance level is 1 the correct value of F from the table is This may have to be approximate If so what did you use 1 12 2 252x0772 11 26 07 Page layout view Exhibit 1 A manager believes that the number of sales that an employee makes is related to the number of years worked and their score on an aptitude test He runs the data below on Minitab and gets the following Employee 1 2 3 4 5 6 7 8 Sales 110 100 90 80 70 60 50 40 Years 11 4 9 6 6 8 2 2 Score 70 100 90 40 80 50 40 10 MTB regress c1 2 c2 c3 Regression Analysis Sales versus Years Score The regression equation is Sales 28 2 3 10 Years Predictor Coef SE Coef Constant 28 19 13 87 Years 3 103 1 984 Score 0 4698 0 2133 S 15 1088 R Sq 72 8 Analysis of Variance Source DF SS Regression 2 3058 6 Residual Error 5 1141 4 Total 7 4200 0 Source Years Score DF 1 1 0 470 Score T P 2 03 0 098 1 56 0 179 2 20 0 079 R Sq adj 62 0 MS 1529 3 228 3 F 6 70 P 0 038 Seq SS 1951 4 1107 3 The sum of the sales column is 600 and the sum of the squared numbers in the sales column is not needed The sum of the years column is 48 and the sum of the squared numbers in the years column is 362 The sum of the score column is 480 and the sum of the squared numbers in the score column is 35200 If Sales is the dependent variable and years and score are the independent variables we have found that the sum of x1y is 3980 and the sum of x1 x2 is 3200 The sum of x2y has not been computed 6 In the multiple regression what coefficients are significant at the 10 significance level 2 7 In the multiple regression what coefficients are significant at the 5 significance level 1 15 8 Assuming that the coefficients in the multiple regression are correct how many sales would we predict for someone with 9 years of experience and a score of 90 1 9 Using the information in the multiple regression printout make your result in 8 into a rough prediction interval 2 10 Using the information in the printout what is the value of R squared for a regression of sales against years alone 2 20 3 252x0772 11 26 07 Page layout view MTB regress c1 2 c2 c3 Regression Analysis Sales versus Years Score The regression equation is Sales 28 2 3 10 Years 0 470 Score Predictor Coef SE Coef T P Constant 28 19 13 87 2 03 0 098 Years 3 103 1 984 1 56 0 179 Score 0 4698 0 2133 2 20 0 079 S 15 1088 R Sq 72 8 R Sq adj 62 0 Exhibit 1 A manager believes that the number of sales that an employee makes is related to the number of years worked and their score on an aptitude test He runs the data below on Minitab and gets the following Employee Sales Years Score 1 110 11 70 2 100 4 100 3 90 9 90 4 80 6 40 5 70 6 80 6 60 8 50 7 50 2 40 8 40 2 10 Analysis of Variance Source DF SS MS F P Regression 2 3058 6 1529 3 6 70 0 038 Residual Error 5 1141 4 228 3 Total 7 4200 0 Source DF Seq SS Years 1 1951 4 Score 1 1107 3 The sum of the sales column is 600 and the sum of the squared numbers in the sales column is not needed The sum of the years column is 48 and the sum of the squared numbers in the years column is 362 The sum of the score column is 480 and the sum of the squared numbers in the score column is 35200 If Sales is the dependent variable and years and score are the independent variables we have found that the sum of x1y is 3980 and the sum of x1 x2 is 3200 The sum of x2y has not been computed 11 Do a simple regression of sales against score alone a Compute the sum xy that you will need for this regression Show your work 2 Don t compute stuff that has already been done for you b It says that you do not need to know the sum of squares in the sales column You do Y 2 nY 2 Without …
View Full Document
Unlocking...