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.

Multiple Independent Variables

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Those individuals who visit doctors are more likely to die than those who do not—a significant and replicable correlation in the literature. Do we have an epidemic of homicidal doctors rampaging through our society? Or is it perhaps an artifact of another variable? Perhaps people who are more ill, and therefore more likely to die, are those most likely to visit doctors. Or perhaps there is a curvilinear effect, such that those most likely to visit doctors are either very health conscious or very ill, and individuals in the former group are much less likely to die and the latter group much more likely to die.

Students who come from families with lower socioeconomic status (SES) are less likely to thrive ...

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