In recent columns we showed how linear regression can be used to predict a continuous dependent variable given other independent variables 1,2. When the dependent variable is categorical, a common ...

Multiple linear regression (MLR) is a statistical technique that uses several explanatory variables to predict the outcome of a response variable.

Based on a statistic proposed by Cook (1977) for the detection of influential observations, we propose a measure for the influence of variables in linear-regression models, and we introduce the ...

There are rarer instances, for example, an example from geology discussed here, where the use of orthogonal regression without proper attention to modeling may lead to either overcorrection or ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of linear regression with two-way interactions between predictor variables. Compared to standard linear ...

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...

Lesson 10 Multiple Linear Regression The purpose of this tutorial is to continue our exploration of regression by constructing linear models with two or more explanatory variables. This is an ...

Parametric versus Semi/nonparametric Regression Models Course Topics Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the ...

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