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.

Simple Linear Models With Continuous Dependent Variables: Simple ANOVA Analyses

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In this chapter, we will cover basic aspects of linear modeling when you have continuous dependent variables (DVs) and an unordered categorical independent variable (IV). This is typically viewed as a simple analysis of variance (ANOVA) chapter in any statistics textbook. In our world of the generalized linear model (GLM), we will view this type of analysis not as a completely separate and alien creature compared with regression/correlation; rather, we will examine it as a simple variant on what we were doing in Chapter 3. By the time you have mastered Chapters 3, 4, and 5, you will be well prepared to take on a broad array of more complex analyses in the ...

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