Response variable selection arises naturally in many applications, but has not been studied as thoroughly as predictor variable selection. In this paper, we discuss response variable selection in both ...

This article is concerned with the selection of subsets of predictor variables in a linear regression model for the prediction of a dependent variable. It is based on a Bayesian approach, intended to ...

Linear regression and feature selection are two such foundational topics. Linear regression is a powerful technique for predicting numbers from other data.

The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...

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 ...

Linear forecasting models can be used in both types of forecasting methods. In the case of causal methods, the causal model may consist of a linear regression with several explanatory variables.

This tells R to find the best model in which the response variable y is a linear function of a set of explanatory variables x1, x2, and so on. I will start with a model I call “model.ks” (to denote an ...

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, ...