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

Logistic regression is a powerful technique for fitting models to data with a binary response variable, but the models are difficult to interpret if collinearity, nonlinearity, or interactions are ...

This paper studies sparse linear regression analysis with outliers in the responses. A parameter vector for modeling outliers is added to the standard linear regression model and then the sparse ...

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

Using Linear Regression Because much economic data has cycles, multiple trends and non-linearity, simple linear regression is often inappropriate for time-series work, according to Yale University.