252x0771 11 26 07 Page layout view ECO252 QBA2 THIRD EXAM November 29 2007 Version 1 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 26 14 1 P 20 x 38 2 P x 0 3 P 32 x 76 4 x 075 1 252x0771 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 is below 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 5 the correct value of F from the table is This may have to be approximate If so what did you use 1 12 2 252x0771 11 26 07 Page layout view Exhibit 1 A realtor believes that the selling price of a home in thousands is related to the condition of the home on a 1 to 10 scale and the size of the home in hundreds of square feet He runs the data below on Minitab and gets the following Row Price Size Condition 1 360 23 5 2 200 11 2 3 340 20 9 4 280 17 3 5 280 15 8 6 330 21 4 7 380 24 7 8 250 13 6 MTB regress c1 2 c2 c3 Regression Analysis Price versus Size Condition The regression equation is Price 64 5 11 7 Size 4 88 Condition Predictor Coef SE Coef T P Constant 64 539 4 228 15 27 0 000 Size 11 7282 0 2317 50 62 0 000 Condition 4 8826 0 4494 S 2 75997 R Sq 99 9 R Sq adj 99 8 Analysis of Variance Source DF SS Regression 2 25712 Residual Error 5 38 Total 7 25750 Source Size Cond DF 1 1 MS 12856 8 F 1687 70 P 0 000 Seq SS 24813 899 The sum of the price column is 2420 and the sum of the squared numbers in the sales column is not needed The sum of the Size column is 144 and the sum of the squared numbers in the Size column is 2750 The sum of the Condition column is 44 and the sum of the squared numbers in the Condition column is 284 If Price is the dependent variable and Size and Condition are the independent variables we have found that the sum of x1y is 45540 and the sum of x1 x2 is 818 The sum of x2y has not been computed 6 and 7 In the multiple regression are the coefficients of size and condition significant at the 5 significance level Give reasons Do not do unneeded computations 2 15 8 Assuming that the coefficients in the multiple regression are correct what price would we predict for a home with 20 hundred square feet and a condition score of 9 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 Price against Size alone 2 20 3 252x0771 11 26 07 Page layout view Exhibit 1 A realtor believes that the selling price of a home in thousands is related to the condition of the home on a 1 to 10 scale and the size of the home in hundreds of square feet He runs the data below on Minitab and gets the following Row Price Size Condition 1 360 23 5 2 200 11 2 3 340 20 9 4 280 17 3 5 280 15 8 6 330 21 4 7 380 24 7 8 250 13 6 MTB regress c1 2 c2 c3 Regression Analysis Price versus Size Condition The regression equation is Price 64 5 11 7 Size 4 88 Condition Predictor Coef SE Coef T P Constant 64 539 4 228 15 27 0 000 Size 11 7282 0 2317 50 62 0 000 Condition 4 8826 0 4494 S 2 75997 R Sq 99 9 R Sq adj 99 8 Analysis of Variance Source DF SS MS F P Regression 2 25712 12856 1687 70 0 000 Residual Erro 5 38 8 Total 7 25750 Source DF Seq SS Size 1 24813 Condition 1 899 The sum of the price column is 2420 and the sum of the squared numbers in the sales column is not needed The sum of the Size column is 2750 and the sum of the squared numbers in the Size column is 2950 The sum of the Condition column is 44 and the sum of the squared numbers in the Condition column is 284 If Price is the dependent variable and Size and Condition are the independent variables we have found that the sum of x1y is 45540 and the sum of x1 x2 is 818 The sum of x2y has not been computed 11 Do a simple regression of Price against Condition 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 doing any …
View Full Document
Unlocking...