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.

Interactions Between Independent Variables: Simple Moderation

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Thus far in the book, you have mastered the art of examining simple relationships between many different combinations of independent variables (IVs) and dependent variables (DVs). In Chapter 8, we began asking more nuanced questions by entering multiple predictors into our regression equations. For example, we can now explore whether particular variables are stronger predictors than others, as well as issues of unique variance accounted for when other variables are controlled for.

However, we are merely beginning to scratch the surface in terms of interesting questions we can ask of our data. Even in multiple regression, we assume that all variables have the same effect across all other groups. In this chapter, we begin to ask questions such ...

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