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.

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 ...

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, ...

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

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

Yannick Saas, Frédéric Gosselin, Comparison of regression methods for spatially-autocorrelated count data on regularly- and irregularly-spaced locations, Ecography, Vol. 37, No. 5 (May 2014), pp.

Learn the difference between linear regression and multiple regression and how investors can use these types of statistical analysis.