Ordinary linear regression (OLR) assumes that response variables are continuous. Generalized Linear Models (GLMs) provide an extension to OLR since response variables can be continuous or discrete ...

Imrey, Koch, Stokes and collaborators (1981) have reviewed the literature of log linear and logistic categorical data modelling, and presented a matrix formulation of log linear models parallel to the ...

Now that you've got a good sense of how to "speak" R, let's use it with linear regression to make distinctive predictions. The R system has three components: a scripting language, an interactive ...

This family includes models for categorical data such as logistic, probit, and complementary log-log regression for binomial data and Poisson regression for count data, as well as continuous models ...

Be sensitive to ethical issues that are involved in dealing with data science applications arising in real world situations. Clearly communicate the results of a data science analysis to a ...

The literature of log linear models and logistic regression is surveyed from a contemporary point of view. A matrix formulation of the general log linear model for product-multinomial random counts is ...

This book discusses categorical data analysis and its implementation with the SAS System. Both nonparametric methods and model-based parametric methods are discussed. Specific topics include ...