Applied regression analysis / Norman R. Draper, Harry Smith.

'A Wiley-Interscience publication.'

Includes bibliographical references and indexes.

0. Basic Prerequisite Knowledge -- 1. Fitting a Straight Line by Least Squares -- 2. Checking the Straight Line Fit -- 3. Fitting Straight Lines: Special Topics -- 4. Regression in Matrix Terms: Straight Line Case -- 5. The General Regression Situation -- 6. Extra Sums of Squares and Tests for Several Parameters Being Zero -- 7. Serial Correlation in the Residuals and the Durbin-Watson Test -- 8. More on Checking Fitted Models -- 9. Multiple Regression: Special Topics -- 10. Bias in Regression Estimates, and Expected Values of Mean Squares and Sums of Squares -- 11. On Worthwhile Regressions, Big F's, and R[superscript 2] -- 12. Models Containing Functions of the Predictors, Including Polynomial Models -- 13. Transformation of the Response Variable -- 14.'Dummy' Variables -- 15. Selecting the'Best' Regression Equation -- 16. Ill-Conditioning in Regression Data -- 17. Ridge Regression -- 18. Generalized Linear Models (GLIM) -- 19. Mixture Ingredients as Predictor Variables -- 20. The Geometry of Least Squares -- 21. More Geometry of Least Squares -- 22. Orthogonal Polynomials and Summary Data -- 23. Multiple Regression Applied to Analysis of Variance Problems -- 24. An Introduction to Nonlinear Estimation -- 25. Robust Regression -- 26. Resampling Procedures (Bootstrapping).

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