Statistics 350 Lecture 23

Today

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Today:

Exam next day • Good Chapter 7 questions: 7.1, 7.2, 7.3, 7.28, 7.29

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Deriving the least squares estimator for

 • Useful result: • Why useful?

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• Mistakes in book:

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• Test for lack of fit: • Full model and reduced model terminology?

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• How to estimate variance of mean response for multiple linear regression model?

• Prediction Variance?

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• Sums of squares…what will I have available?

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• How do I compute the extra sums of squares?

Examples from Chapter 7

• 7.29 a • Show that SSR(X 1 , X 2 , X 3 , X 4 )=SSR(X 2 , X 3 ) + SSR(X 1 | X 2 , X 3 ) + SSR(X 4 | X 1 , X 2 , X 3 )

Examples from Chapter 7

• 7.28 b • For the multiple linear regression model with 5 variables, what is the relevant extra sum of squares for testing whether or not  5 =0 ?