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.

Prediction in the Generalized Linear Model

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Imagine you have developed a great new intervention to, say, help students with reading difficulties or to help obese people lose weight. Your initial research shows that it works well, but it is extremely expensive and so only those who are most likely to benefit from your intervention should have access to it. How are you going to decide who should get the intervention?

Or imagine you are in charge of admissions for your college or university. You know that certain things predict success in your school, at least partly. How do you decide who gets admitted?

Most research we hear about is explanatory, meaning that the goal of the research is attempting to understand a phenomenon. For example, ...

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