In the accompanying table, we give the smallest SSE for each number of predictors k ( k = 1, 2, 3, 4) for a regression problem in which y = cumulative heat of hardening in cement, x 1 = % tricalcium aluminate, x 2 = % tricalcium silicate, x 3 = % aluminum ferrate, and x 4 = % dicalcium silicate. Number of Predictors k Predictor(s) SSE 1 x 4 880.85 2 x 1 ,x 2 58.01 3 x 1 , x 2 ,x 3 49.20 4 x 1, x 2 ,x 3 ,x 4 47.86 In addition, n = 13 and SST = 2715.76. a. Use the criteria discussed in the text to recommend the use of a particular regression model. b. Would forward selection result in the best twopredictor model? Explain.

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Probability and Statistics for Eng...

9th Edition
Jay L. Devore
Publisher: Cengage Learning
ISBN: 9781305251809
BuyFind

Probability and Statistics for Eng...

9th Edition
Jay L. Devore
Publisher: Cengage Learning
ISBN: 9781305251809

Solutions

Chapter 13.5, Problem 57E
Textbook Problem

In the accompanying table, we give the smallest SSE for each number of predictors k (k = 1, 2, 3, 4) for a regression problem in which y = cumulative heat of hardening in cement, x1 = % tricalcium aluminate, x2 = % tricalcium silicate, x3 = % aluminum ferrate, and x4 = % dicalcium silicate.

Number of Predictors k Predictor(s) SSE
1 x4 880.85
2 x1,x2 58.01
3 x1,x2,x3 49.20
4 x1, x2,x3,x4 47.86

In addition, n = 13 and SST = 2715.76.

a.    Use the criteria discussed in the text to recommend the use of a particular regression model.

b.    Would forward selection result in the best twopredictor model? Explain.

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Chapter 13 Solutions

Probability and Statistics for Engineering and the Sciences
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