Purchasing department cost drivers, multiple regression analysis (continuation of Problem 10-35). Sherry Jones decides that the simple regression analysis used in Problem 10-35 could be extended to a multiple regression analysis. She finds the following results for several multiple regressions:

REQUIRED 1. Evaluate regression 4 using the economic plausibility, goodness of fit, significance of independent variables, and specification analysis criteria. Compare regression 4 with regressions 2 and 3 in Problem 10-35. Which model would you recommend that Jones use? Why?
2. Compare regression 5 with regression 4. Which model would you recommend that Jones use? Why?
3. Jones estimates the following data for the Saskatoon store for next year: dollar value of merchandise purchased, $75,000,000; number of purchase orders, 3,900; number of suppliers, 110. How much should Jones budget for purchasing department costs for the Saskatoon store for next year?
4. What difficulties may arise in multiple regressions that do not arise in simple regressions?
. Is there evidence of such difficulties in either of the multiple regressions presented in this problem?
5. Give two examples of decisions where the regression results reported here (and in Problem LO1

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Regression 4. PDC= a + (b, x no. of POs) + (b x no. of Ss) Variable Coefficient Standard Error t-Value Constant $485,384 $257,477 1.89 Independent variable 1: no. of POS $ 123.22 $ 57.69 2.14 Independent variable 2: no. of Ss $ 2,952 $ 1,476 2.00 2 = 0.63; Durbin-Watson statistic = 1.90 Regression 5. PDC=a+ (b, x no. of POs) + (b x no. of Ss) + (b3xMPS) Variable Coefficient Standard Error t-Value Constant $494,684 $310,205 1.59 Independent variable 1: no. of POS $ 124.05 $ 63.49 1.95 Independent variable 2: no. of Ss $ 2,984 $ 1,622 1.84 -0.0002 0.0030 -0.07 Independent variable 3: MPS 2=0.63; Durbin-Watson statistic = 1.90 The coefficients of correlation between combinations of pairs of the variables are