Applied Regression Analysis / Edition 2

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More About This Textbook

Overview

A major goal of scientific exploration is the discovery of relationships among variables. Regression is the analysis or measure of the relationship between a dependent variable and one or more independent variables. This text covers a commonly used statistical tool in constructing mathematical models from experimental data.

"...offers an introduction to the fundamentals of regression analysis, focusing on the fitting & checking of both linear & nonlinear regression models with computers/calculators... includes carefully designed exercise sets, computer disk."

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Editorial Reviews

Statistical Methods in Medical Research
I would wholeheartedly recommend this book to any statistician. The third edition has many advantages over the second.
Statistical Methods in Medical Research
I would wholeheartedly recommend this book to any statistician. The third edition has many advantages over the second.
Booknews
New edition of a text offering an accessilbe introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, it focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. An included disk contains data files for the examples used in the chapters and for the exercises. Annotation c. by Book News, Inc., Portland, Or.
From the Publisher

"I would wholeheartedly recommend this book to any statistician. The third edition has many advantages over the second." (Statistical Methods in Medical Research, Vol. 9, 5)

"this is an excellently written book" (Statistics & Decisions, Vol. 19, No.3, 2001)

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Product Details

  • ISBN-13: 9780471029953
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 3/28/1981
  • Series: Probability and Statistics Series
  • Edition description: Older Edition
  • Edition number: 2
  • Pages: 736
  • Product dimensions: 6.35 (w) x 9.33 (h) x 1.55 (d)

Meet the Author

NORMAN R. DRAPER teaches in the Department of Statistics at the University of Wisconsin. HARRY SMITH is a former faculty member of the Mt. Sinai School of Medicine.

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Table of Contents

Preface
About the Software
0 Basic Prerequisite Knowledge 1
1 Fitting a Straight Line by Least Squares 15
2 Checking the Straight Line Fit 47
3 Fitting Straight Lines: Special Topics 79
4 Regression in Matrix Terms: Straight Line Case 115
5 The General Regression Situation 135
6 Extra Sums of Squares and Tests for Several Parameters Being Zero 149
7 Serial Correlation in the Residuals and the Durbin-Watson Test 179
8 More on Checking Fitted Models 205
9 Multiple Regression: Special Topics 217
10 Bias in Regression Estimates, and Expected Values of Mean Squares and Sums of Squares 235
11 On Worthwhile Regressions, Big F's, and R[superscript 2] 243
12 Models Containing Functions of the Predictors, Including Polynomial Models 251
13 Transformation of the Response Variable 277
14 "Dummy" Variables 299
15 Selecting the "Best" Regression Equation 327
16 Ill-Conditioning in Regression Data 369
17 Ridge Regression 387
18 Generalized Linear Models (GLIM) 401
19 Mixture Ingredients as Predictor Variables 409
20 The Geometry of Least Squares 427
21 More Geometry of Least Squares 447
22 Orthogonal Polynomials and Summary Data 461
23 Multiple Regression Applied to Analysis of Variance Problems 473
24 An Introduction to Nonlinear Estimation 505
25 Robust Regression 567
26 Resampling Procedures (Bootstrapping) 585
Bibliography 593
True/False Questions 605
Answers to Exercises 609
Tables 684
Index of Authors Associated with Exercises 695
Index 697
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