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, ...

Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with ...

Scandinavian Journal of Statistics, Vol. 43, No. 4 (December 2016), pp. 1035-1045 (11 pages) Linear structural equation models, which relate random variables via linear inter-dependencies and Gaussian ...

In the conventional general linear model it is often possible to examine a more complete model that includes nonlinear terms. This is an extension of a test for non-additivity by Tukey and a ...

Linear regression remains a cornerstone of statistical analysis, offering a framework for modelling relationships between a dependent variable and one or more independent predictors. Over the past ...