Introduction to Linear Regression Analysis, 6e Solutions Manual
Description
Introduction to Linear Regression Analysis, 6th Edition is the most comprehensive, fulsome, and current examination of the foundations of linear regression analysis. Fully updated in this new sixth edition, the distinguished authors have included new material on generalized regression techniques and new examples to help the reader understand retain the concepts taught in the book.
The new edition focuses on four key areas of improvement over the fifth edition:
* New exercises and data sets
* New material on generalized regression techniques
* The inclusion of JMP software in key areas
* Carefully condensing the text where possible
Introduction to Linear Regression Analysis skillfully blends theory and application in both the conventional and less common uses of regression analysis in today's cutting-edge scientific research. The text equips readers to understand the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences.
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Persons
DOUGLAS C. MONTGOMERY, PHD, is Regents Professor of Industrial Engineering and Statistics at Arizona State University. Dr. Montgomery is the co-author of several Wiley books including Introduction to Linear Regression Analysis, 5th Edition.
ELIZABETH A. PECK, PHD, is Logistics Modeling Specialist at the Coca-Cola Company in Atlanta, Georgia.
G. GEOFFREY VINING, PHD, is Professor in the Department of Statistics at Virginia Polytechnic and State University. Dr. Peck is co-author of Introduction to Linear Regression Analysis, 5th Edition.
Content
Preface vii
2 Simple Linear Regression 1
3 Multiple Linear Regression 13
4 Model Adequacy Checking 29
5 Transformations and Weighting to Correct Model Inadequacies 59
6 Diagnostics for Leverage and Influence 74
7 Polynomial Regression Models 79
8 Indicator Variables 86
9 Multicollinearity 95
10 Variable Selection and Model Building 100
11 Validation of Regression Models 105
12 Introduction to Nonlinear Regression 108
13 Generalized Linear Models 113
14 Regression Analysis of Time Series Data 121
15 Other Topics in the Use of Regression Analysis 125
