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.

A Nerdly Manifesto

When I was in graduate school, my adviser and I were analyzing data from an experiment where we manipulated academic feedback to university students. Students were assigned to “succeed” or “fail” on an academic-type task, largely because we randomly assigned them (without their knowledge) to receive one of two versions of a common anagram-type task. One version contained a large percentage of anagrams that were impossible to solve, whereas the other contained many anagrams that should have been easy to solve. Thus, at the end of the timed experiment, about half of the students felt they had not done well, and about half felt they had done well.1 We evaluated whether their self-esteem was influenced by these experiences, and as a ...

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