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

In theory, a linear regression with interactions model can be trained using a closed-form solution that involves computing a matrix inverse. But in practice, a model is usually trained using iterative ...

The purpose of this tutorial is to continue our exploration of multivariate statistics by conducting a simple (one explanatory variable) linear regression analysis. We will continue to use the ...

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, ...

10.3.1 Scatterplot matrix Recall that we use SAS’s scatterplot matrix feature to quickly scan for pairs of explanatory variables that might be colinear. To do this in R we must first make sure we ...

Multiple regression and regression diagnostics. Generalised linear models; the exponential family, the linear predictor, link functions, analysis of deviance, parameter estimation, deviance residuals.

The Cholesky matrix penalization estimator inherits the advantages of the matrix lasso and the lasso sliced inverse regression estimator. Furthermore, it shows superior performance in numerical ...

We consider the multivariate response regression problem with a regression coefficient matrix of low, unknown rank. In this setting, we analyze a new criterion for selecting the optimal reduced rank.