Linear regression assumes a linear relationship, is sensitive to outliers, and may not perform well if the assumptions (like homoscedasticity or normality) are violated.

Discover how linear regression works, from simple to multiple linear regression, with step-by-step examples, graphs and real-world applications.

An accountant can use linear regression only if he can apply the linearity assumption to the cost he is predicting.

Journal of Applied Econometrics, Vol. 24, No. 4 (Jun. - Jul., 2009), pp. 651-674 (24 pages) We consider the problem of variable selection in linear regression models. Bayesian model averaging has ...

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

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, ...

We incorporate a reduced-rank envelope in an elliptical multivariate linear regression to improve the efficiency of estimation. The reduced-rank envelope model takes advantage of both a reduced-rank ...