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.

Basic Estimation and Assumptions

Two types of estimation will be presented in this book: ordinary least squares (OLS) and maximum likelihood (ML) estimation. As we discussed in Chapter 1, there are a variety of statistical procedures that have been traditionally treated as separate, such as regression, analysis of variance (ANOVA), and logistic regression. However, the argument we will continue to develop is the idea that these are all different and complementary aspects of the generalized linear model (GLM). Although it may seem odd to have just finished Chapter 1 with an admonition that we should train researchers and statisticians in a modern and applied way and then launch into a chapter on estimation, it is critical for anyone performing statistical analyses to understand the ...

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