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.

Simple Curvilinear Models

Advanced Organizer

In previous chapters, we talked about the assumption in almost any type of linear modeling that the model is, well, linear. Whether we are talking about ordinary least squares (OLS) regression or logit or probit models, linear is in the label. However, I think that in many areas of science, this assumption may not be tenable or even desirable. I believe that if we routinely looked for curvilinear relationships, we would find many. In fact, while writing this chapter, I had to explore surprisingly few examples to produce the curvilinear results shown herein. The fact of the matter is that curves are everywhere, and I hope this chapter encourages you to begin looking for them in your data. You will ...

  • Loading...
locked icon

Sign in to access this content

Get a 30 day FREE TRIAL

  • Watch videos from a variety of sources bringing classroom topics to life
  • Read modern, diverse business cases
  • Explore hundreds of books and reference titles