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 ...

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 ...

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

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

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, ...

Multiple and Non-Linear Regression The variable you are trying to estimate is referred to as dependent, while the variable you use in the model to predict the dependent variable is called independent.

Regression analysis is a quantitative tool that is easy to use and can provide valuable information on financial analysis and forecasting.