Multiple Regression: A Practical Introduction is a text for an advanced undergraduate or beginning graduate course in statistics for social science and related fields. Also, students preparing for more advanced courses can self-study the text to refresh and solidify their statistical background. Drawing on decades of teaching this material, the authors present the ideas in an approachable and nontechnical manner, with no expectation that readers have more than a standard introductory statistics course as background. Multiple regression asks how a
dependent variable is related to, or predicted by, a set of
independent variables. The book includes many interesting example analyses and interpretations, along with exercises. Each dataset used for the examples and exercises is small enough for readers to easily grasp the entire dataset and its analysis with respect to the specific statistical techniques covered.
A website for the book includes SPSS, Stata, SAS, and R code and commands for each type of analysis or recoding of variables in the book. Solutions to two of the end-of-chapter exercise types are also available for students to practice. The instructor side of the site contains editable Power Point slides, other solutions, and a test bank.
Table of Content
Chapter 1 Introduction
Chapter 2 Fundamentals of Multiple Regression
Chapter 3 Categorical Independent Variables in Multiple Regression: Dummy Variables
Chapter 4 Multiple Regression with Interaction
Chapter 5 Logged Variables in Multiple Regression
Chapter 6 Nonlinear Relationships in Multiple Regression
Chapter 7 Categorical Dependent Variables: Logistic Regression
Chapter 8 Count Dependent Variables: Poisson Regression
Chapter 9 A Brief Tour of Some Related Methods
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