Course TopicsIn this short course we will cover how to analyze simple and multiple linear regression models. You will learn concepts in linear regression such as:1) How to use the F-test to determine ...

When multiple variables are associated with a response, the interpretation of a prediction equation is seldom simple.

Experienced SAS System users will find this an invaluable guide to SAS procedures for performing regression analyses. Simple and multiple variable models are discussed as well as polynomial models, ...

First, multiple linear regression models are considered and the design matrices are allowed to be different. Second, the predictor variables are either unconstrained or constrained to finite intervals ...

Conclusions: Generalised linear models are attractive for the regression of cost data because they provide parametric methods of analysis where a variety of non-normal distributions can be specified ...

Defines the least-squares means for the fixed-effects general linear model. The report also discusses the use of least-squares means in lieu of class or subclass arithmetic means with unbalanced ...

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, ...