Applied Regression Analysis

ISBN-10: 0471170828

ISBN-13: 9780471170822

Edition: 3rd 1998 (Revised)

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Description: Regression analysis is a commonly used statistical tool in the construction of mathematical models from experimental data. This is a new and expanded edition of a classic text on the subject.

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Book details

List price: $200.95
Edition: 3rd
Copyright year: 1998
Publisher: John Wiley & Sons, Incorporated
Publication date: 4/23/1998
Binding: Hardcover
Pages: 736
Size: 7.50" wide x 10.50" long x 1.75" tall
Weight: 3.630
Language: English

GEORGE E. P. BOX, PhD, DSc, FRS, is Ronald Aylmer Fisher Professor Emeritus of Statistics and Industrial Engineering at the University of Wisconsin-Madison. He is a Fellow of the Royal Society, an Honorary Fellow and Shewhart and Deming Medalist of the American Society for Quality, and was awarded the Guy Medal in Gold from the Royal Statistical Society. He is also the recipient of the Samuel S. Wilks Memorial Medal of the American Statistical Association.NORMAN DRAPER is Professor Emeritus of Statistics in the Department of Statistics at the University of Wisconsin, Madison. He received his PhD from the University of North Carolina, Chapel Hill, and has published dozens of papers in professional journals. He is the coauthor (with Harry Smith) of Applied Regression Analysis, Third Edition, published by Wiley.

Basic Prerequisite Knowledge
Fitting a Straight Line by Least Squares
Checking the Straight Line Fit
Fitting Straight Lines: Special Topics
Regression in Matrix Terms: Straight Line Case
The General Regression Situation
Extra Sums of Squares and Tests for Several Parameters Being Zero
Serial Correlation in the Residuals and the Durbin-Watson Test
More of Checking Fitted Models
Multiple Regression: Special Topics
Bias in Regression Estimates, and Expected Values of Mean Squares and Sums of Squares
On Worthwhile Regressions, Big F's, and R 2
Models Containing Functions of the Predictors, Including Polynomial Models
Transformation of the Response Variable
"Dummy" Variables
Selecting the "Best" Regression Equation
Ill-Conditioning in Regression Data
Ridge Regression
Generalized Linear Models (GLIM)
Mixture Ingredients as Predictor Variables
The Geometry of Least Squares
More Geometry of Least Squares
Orthogonal Polynomials and Summary Data
Multiple Regression Applied to Analysis of Variance Problems
An Introduction to Nonlinear Estimation
Robust Regression
Resampling Procedures (Bootstrapping)
Bibliography
True/False Questions
Answers to Exercises
Tables
Indexes
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