ESBE4eAISEsm13

# ESBE4eAISEsm13 - Chapter 13 Multiple Regression Learning...

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Chapter 13 Multiple Regression Learning Objectives 1. Understand how multiple regression analysis can be used to develop relationships involving one dependent variable and several independent variables. 2. Be able to interpret the coefficients in a multiple regression analysis. 3. Know the assumptions necessary to conduct statistical tests involving the hypothesized regression model. 4. Understand the role of computer packages in performing multiple regression analysis. 5. Be able to interpret and use computer output to develop the estimated regression equation. 6. Be able to determine how good a fit is provided by the estimated regression equation. 7. Be able to test for the significance of the regression equation. 8. Understand how multicollinearity affects multiple regression analysis. 13 - 1 This edition is intended for use outside of the U.S. only, with content that may be different from the U.S. Edition. This may not be resold, copied, or distributed without the prior consent of the publisher.

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Chapter 13 Solutions: 1. a. b 1 = .5906 is an estimate of the change in y corresponding to a 1 unit change in x 1 when x 2 is held constant. b 2 = .4980 is an estimate of the change in y corresponding to a 1 unit change in x 2 when x 1 is held constant. 2. a. The estimated regression equation is = 45.06 + 1.94 x ˆ y 1 An estimate of y when x 1 = 45 is = 45.06 + 1.94(45) = 132.36 ˆ y b. The estimated regression equation is = 85.22 + 4.32 x ˆ y 2 An estimate of y when x 2 = 15 is = 85.22 + 4.32(15) = 150.02 ˆ y c. The estimated regression equation is = -18.37 + 2.01 x ˆ y 1 + 4.74 x 2 An estimate of y when x 1 = 45 and x 2 = 15 is = -18.37 + 2.01(45) + 4.74(15) = 143.18 ˆ y 3. a. b 1 = 3.8 is an estimate of the change in y corresponding to a 1 unit change in x 1 when x 2 , x 3 , and x 4 are held constant. b 2 = -2.3 is an estimate of the change in y corresponding to a 1 unit change in x 2 when x 1 , x 3 , and x 4 are held constant. b 3 = 7.6 is an estimate of the change in y corresponding to a 1 unit change in x 3 when x 1 , x 2 , and x 4 are held constant. b 4 = 2.7 is an estimate of the change in y corresponding to a 1 unit change in x 4 when x 1 , x 2 , and x 3 are held constant. 4. a. = 25 + 10(15) + 8(10) = 255; sales estimate: \$255,000 ˆ y b. Sales can be expected to increase by \$10 for every dollar increase in inventory investment when advertising expenditure is held constant. Sales can be expected to increase by \$8 for every dollar increase in advertising expenditure when inventory investment is held constant. 13 - 2 This edition is intended for use outside of the U.S. only, with content that may be different from the U.S. Edition. This may not be resold, copied, or distributed without the prior consent of the publisher.
Multiple Regression 5. a. The Minitab output is shown below: The regression equation is Revenue = 88.6 + 1.60 TVAdv Predictor Coef SE Coef T P Constant 88.638 1.582 56.02 0.000 TVAdv 1.6039 0.4778 3.36 0.015 S = 1.215 R-Sq = 65.3% R-Sq(adj) = 59.5% Analysis of Variance Source DF SS MS F P Regression 1 16.640 16.640 11.27 0.015 Residual Error 6 8.860 1.477 Total 7 25.500 b. The Minitab output is shown below: The regression equation is Revenue = 83.2 + 2.29 TVAdv + 1.30 NewsAdv Predictor Coef SE Coef T P Constant 83.230 1.574 52.88 0.000 TVAdv 2.2902 0.3041 7.53 0.001 NewsAdv 1.3010 0.3207 4.06 0.010 S = 0.6426 R-Sq = 91.9% R-Sq(adj) = 88.7%

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## This note was uploaded on 05/26/2010 for the course ACC 251 taught by Professor Carl during the Winter '09 term at University of Central Arkansas.

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ESBE4eAISEsm13 - Chapter 13 Multiple Regression Learning...

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