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In a conversational tone, Regression &amp Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. Author Jason W. Osborne returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.
Preface
Acknowledgments
About the Author
A Nerdly Manifesto
Basic Estimation and Assumptions
Simple Linear Models with Continuous Dependent Variables: Simple Regression Analyses
Simple Linear Models with Continuous Dependent Variables: Simple Anova Analyses
Simple Linear Models with Categorical Dependent Variables: Binary Logistic Regression
Simple Linear Models with Polytomous Categorical Dependent Variables: Multinomial and Ordinal Logistic Regression
Simple Curvilinear Models
Multiple Independent Variables
Interactions Between Independent Variables: Simple Moderation
Curvilinear Interactions Between Independent Variables
Poisson Models: Low-Frequency Count Data as Dependent Variables
Log-Linear Models: General Linear Models when All of Your Variables are Unordered Categorical
A Brief Introduction to Hierarchical Linear Modeling
Missing Data in Linear Modeling
Trustworthy Science: Improving Statistical Reporting
Reliable Measurement Matters
Prediction in the Generalized Linear Model
Modeling in Large, Complex Samples: The Importance of using Appropriate Weights and Design Effect Compensation
A Brief User’s Guide to Z-Scores
Author Index
Subject Index