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.

Log-Linear Models: General Linear Models When All of Your Variables Are Unordered Categorical

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How would you analyze your data if you wanted to explore whether students were retained or dropped out as a function of only categorical variables (like race, sex, or marijuana use)? Logistic regression would certainly be an option; however, with variables like race, which can often lead to multiple dummy variables, interactions can become cumbersome to test. The same issue is present when you have a dependent variable (DV) that is continuous and only categorical independent variables (IVs). Analysis of variance (ANOVA) is a great solution for that situation, because it prevents a good many headaches when you are looking at interactions. Because you already know that it is a ...

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