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 ...

In this paper, we consider the problem of determining the number of structural changes in multiple linear regression models via group fused Lasso. We show that with probability tending to one, our ...

You perform a multiple linear regression analysis when you have more than one explanatory variable for consideration in your model. You can write the multiple linear regression equation for a model ...

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, ...

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

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, ...