Applied Regression Analysis, Volume 1

Front Cover
Wiley, Apr 23, 1998 - Mathematics - 706 pages
An outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures. Extensive support materials include sets of carefully designed exercises with full or partial solutions and a series of true/false questions with answers. All data sets used in both the text and the exercises can be found on the companion disk at the back of the book. For analysts, researchers, and students in university, industrial, and government courses on regression, this text is an excellent introduction to the subject and an efficient means of learning how to use a valuable analytical tool. It will also prove an invaluable reference resource for applied scientists and statisticians.

Contents

O Basic Prerequisite Knowledge
1
Fitting a Straight Line by Least Squares
15
Checking the Straight Line
47
15
70
Special Topics
79
Straight Line Case
115
The General Regression Situation
135
Extra Sums of Squares and Tests for Several Parameters
149
277
380
Ridge Regression
387
Generalized Linear Models GLIM
401
Mixture Ingredients as Predictor Variables
409
Appendix 19A Transforming k Mixture Variables to k 1 Working
422
More Geometry of Least Squares
447
Orthogonal Polynomials and Summary Data
461
Multiple Regression Applied to Analysis of Variance Problems
473

115
169
Serial Correlation in the Residuals and the DurbinWatson Test
179
135
201
More on Checking Fitted Models
205
Special Topics
217
Bias in Regression Estimates and Expected Values of Mean
235
On Worthwhile Regressions Big Fs and
243
Models Containing Functions of the Predictors Including
251
Transformation of the Response Variable
277
Dummy Variables
299
Selecting the Best Regression Equation
327
235
358
149
364
243
476
An Introduction to Nonlinear Estimation
505
Robust Regression
567
461
574
Resampling Procedures Bootstrapping
585
Additional Comments
591
TrueFalse Questions
605
299
645
327
684
473
686
Index of Authors Associated with Exercises
695
369
698
Copyright

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