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

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

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, ...

Linear regression was used to model the relationship between log-transformed pulse pressure and other variables, with interactions tested using a likelihood ratio test.