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About this book
In a conversational tone, Regression & 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. The author 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.
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Yes, you can access Regression & Linear Modeling by Jason W. Osborne in PDF and/or ePUB format, as well as other popular books in Psychology & Research & Methodology in Psychology. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Cover
- Half Title
- Publisher Note
- Title Page
- Copyright Page
- Brief Contents
- Detailed Contents
- Preface
- Acknowledgments
- About the Author
- 1 A Nerdly Manifesto
- 2 Basic Estimation and Assumptions
- 3 Simple Linear Models With Continuous Dependent VariablesSimple Regression Analyses
- 4 Simple Linear Models With Continuous Dependent VariablesSimple ANOVA Analyses
- 5 Simple Linear Models With Categorical Dependent VariablesBinary Logistic Regression
- 6 Simple Linear Models With Polytomous Categorical Dependent VariablesMultinomial and Ordinal Logistic Regression
- 7 Simple Curvilinear Models
- 8 Multiple Independent Variables
- 9 Interactions Between Independent VariablesSimple Moderation
- 10 Curvilinear Interactions Between Independent Variables
- 11 Poisson ModelsLow-Frequency Count Data as Dependent Variables
- 12 Log-Linear ModelsGeneral Linear Models When All of Your Variables Are Unordered Categorical
- 13 A Brief Introduction to Hierarchical Linear Modeling
- 14 Missing Data in Linear Modeling
- 15 Trustworthy ScienceImproving Statistical Reporting
- 16 Reliable Measurement Matters
- 17 Prediction in the Generalized Linear Model
- 18 Modeling in Large, Complex SamplesThe Importance of Using Appropriate Weights and Design Effect Compensation
- Appendix A A Brief User’s Guide to z-Scores
- Author Index
- Index