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 Linear Models With Continuous Dependent Variables: Simple Regression Analyses

Advance Organizer

In this chapter, we will cover some of the basic aspects of linear modeling: evaluating the simple relationship between two variables in which both the independent variable (IV) and the dependent variable (DV) are continuous. This is typically viewed as the “simple regression” chapter in any statistics textbook; however, in our world of the generalized linear model (GLM), this chapter will provide the foundation for just about all other analyses to come, including analysis of variance (ANOVA) (Chapter 4, in which we will demonstrate that you get the same results regardless of whether you analyze the data via ANOVA or regression), logistic regression (Chapter 5, wherein we model binary DVs), and most chapters ...

  • 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