You construct a generalized linear model by deciding on response and explanatory variables for your data and choosing an appropriate link function and response probability distribution. Some examples ...

This is easily handled in a regression framework ... to the qualitative variables in order to account for correlation in the data and reduce MSE; however, the quantitative variables are not of primary ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

This chapter is an introduction to regression and procedures for finding the best curve to fit a set of data. We will discuss linear and parabolic regression, and regression with power series ...

A basic understanding of the linear regression model with matrix algebra is assumed ... The book makes frequent use of numerical examples based on generated data to illustrate the key models and ...