Applied Regression Analysis and Other Multivariable Methods

✍ Scribed by David G. Kleinbaum, Lawrence L. Kupper, Azhar Nizam, Eli S. Rosenberg

• Publisher: Cengage Learning
• Year: 2013
• Tongue: English
• Leaves: 1074
• Edition: 5
• Category: Library

✦ Synopsis

This bestseller will help you learn regression-analysis methods that you can apply to real-life problems. It highlights the role of the computer in contemporary statistics with numerous printouts and exercises that you can solve using the computer. The authors continue to emphasize model development, the intuitive logic and assumptions that underlie the techniques covered, the purposes, advantages, and disadvantages of the techniques, and valid interpretations of those techniques.

✦ Table of Contents

Applied Regression Analysis and Other Multivariable Methods
Title Page
DEDICATIONS
CONTENTS
PREFACE
1 Concepts and Examples of Research
2 Classification of Variables and the Choice of Analysis
3 Basic Statistics: A Review
4 Introduction to Regression Analysis
5 Straight-line Regression Analysis
6 The Correlation Coefficient and Straight-line Regression Analysis
7 The Analysis-of-Variance Table
8 Multiple Regression Analysis: General Considerations
9 Statistical Inference in Multiple Regression
10 Correlations: Multiple, Partial, and Multiple Partial
11 Confounding and Interaction in Regression
12 Dummy Variables in Regression
13 Analysis of Covariance and Other Methods for Adjusting Continuous Data
14 Regression Diagnostics
15 Polynomial Regression
16 Selecting the Best Regression Equation
17 One-way Analysis of Variance
18 Randomized Blocks: Special Case of Two-way ANOVA
19 Two-way ANOVA with Equal Cell Numbers
20 Two-way ANOVA with Unequal Cell Numbers
21 The Method of Maximum Likelihood
22 Logistic Regression Analysis
23 Polytomous and Ordinal Logistic Regression
24 Poisson Regression Analysis
25 Analysis of Correlated Data Part 1: The General Linear Mixed Model
26 Analysis of Correlated Data Part 2: Random Effects and Other Issues
27 Sample Size Planning for Linear and Logistic Regression and Analysis of Variance
A Appendix—Tables
B Appendix—Matrices and Their Relationship to Regression Analysis
C SAS Computer Appendix
D Appendix—Answers to Selected Problems
INDEX