795 c no it is 160 in part a and 229 above in part b

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6.795 c. No, it is 1.60 in part (a) and 2.29 above. In part (b) it represents the marginal change in revenue due to an increase in television advertising with newspaper advertising held constant. d. Revenue = 83.2 + 2.29(3.5) + 1.30(1.8) = $93.56 or $93,560 6. a. The Minitab output is shown below: The regression equation is Speed = 49.8 + 0.0151 Weight Predictor Coef SE Coef T P Constant 49.78 19.11 2.61 0.021 Weight 0.015104 0.006005 2.52 0.025 S = 7.000 R-Sq = 31.1% R-Sq(adj) = 26.2%
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Chapter 15 15 - 4 Analysis of Variance Source DF SS MS F P Regression 1 309.95 309.95 6.33 0.025 Residual Error 14 686.00 49.00 Total 15 995.95 b. The Minitab output is shown below: The regression equation is Speed = 80.5 - 0.00312 Weight + 0.105 Horsepwr Predictor Coef SE Coef T P Constant 80.487 9.139 8.81 0.000 Weight -0.003122 0.003481 -0.90 0.386 Horsepwr 0.10471 0.01331 7.86 0.000 S = 3.027 R-Sq = 88.0% R-Sq(adj) = 86.2% Analysis of Variance Source DF SS MS F P Regression 2 876.80 438.40 47.83 0.000 Residual Error 13 119.15 9.17 Total 15 995.95 c. ˆ y = 80.5 - 0.00312(2910) + 0.105(296) = 102.5 7. a. The Minitab output is shown below: The regression equation is Price = 356 - 0.0987 Capacity + 123 Comfort Predictor Coef SE Coef T P Constant 356.1 197.2 1.81 0.114 Capacity -0.09874 0.04588 -2.15 0.068 Comfort 122.87 21.80 5.64 0.001 S = 51.14 R-Sq = 83.2% R-Sq(adj) = 78.4% Analysis of Variance Source DF SS MS F P Regression 2 90548 45274 17.31 0.002 Residual Error 7 18304 2615 Total 9 108852 b. b 1 = -.0987 is an estimate of the change in the price with respect to a 1 cubic inch change in capacity with the comfort rating held constant. b 2 = 123 is an estimate of the change in the price with respect to a 1 unit change in the comfort rating with the capacity held constant. c. ˆ y = 356 - .0987(4500) + 123 (4) = 404
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Multiple Regression 15 - 5 8. a. The Minitab output is shown below: The regression equation is Return = 247 - 32.8 Safety + 34.6 ExpRatio Predictor Coef SE Coef T P Constant 247.4 110.4 2.24 0.039 Safety -32.84 13.95 -2.35 0.031 ExpRatio 34.59 14.13 2.45 0.026 S = 16.98 R-Sq = 58.2% R-Sq(adj) = 53.3% Analysis of Variance Source DF SS MS F P Regression 2 6823.2 3411.6 11.84 0.001 Residual Error 17 4899.7 288.2 Total 19 11723.0 b. ˆ 247 32.8(7.5) 34.6(2) 70.2 y = + = 9. a. The Minitab output is shown below: The regression equation is %College = 26.7 - 1.43 Size + 0.0757 SatScore Predictor Coef SE Coef T P Constant 26.71 51.67 0.52 0.613 Size -1.4298 0.9931 -1.44 0.170 SatScore 0.07574 0.03906 1.94 0.072 S = 12.42 R-Sq = 38.2% R-Sq(adj) = 30.0% Analysis of Variance Source DF SS MS F P Regression 2 1430.4 715.2 4.64 0.027 Residual Error 15 2312.7 154.2 Total 17 3743.1 b. ˆ y = 26.7 - 1.43(20) + 0.0757(1000) = 73.8 Estimate is 73.8% 10. a. The Minitab output is shown below: The regression equation is Revenue = 33.3 + 7.98 Cars Predictor Coef SE Coef T P Constant 33.34 83.08 0.40 0.695 Cars 7.9840 0.6323 12.63 0.000 S = 226.7 R-Sq = 92.5% R-Sq(adj) = 91.9%
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Chapter 15 15 - 6 Analysis of Variance Source DF SS MS F P Regression 1 8192067 8192067 159.44 0.000 Residual Error 13 667936 51380 Total 14 8860003 b. An increase of 1000 cars in service will result in an increase in revenue of $7.98 million. c. The Minitab output is shown below: The regression equation is Revenue = 106 + 8.94 Cars - 0.191 Location Predictor Coef SE Coef T P Constant 105.97 85.52 1.24 0.239 Cars 8.9427 0.7746 11.55 0.000 Location -0.1914 0.1026 -1.87 0.087 S = 207.7 R-Sq = 94.2% R-Sq(adj) = 93.2% Analysis of Variance Source DF SS MS F P Regression 2 8342186 4171093 96.66 0.000 Residual Error 12 517817 43151 Total 14 8860003 11. a. SSE = SST - SSR = 6,724.125 - 6,216.375 = 507.75 b. 2 SSR 6,216.375 .924 SST 6,724.125 R = = = c. 2 2 1 10 1 1 (1 ) 1 (1 .924) .902 1 10 2 1 a n R R n p = = = d. The estimated regression equation provided an excellent fit.
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