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osborne j regression linear modeling best practices modern methods 2016

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Textbook in PDF format 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. 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

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osborne j regression linear modeling best practices modern methods 2016

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