Linear regression captures the relationship between two variables—for example, the relationship between the daily change in a company's stock prices and the daily change in trading volume.

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

You perform a multiple linear regression analysis when you have more than one explanatory variable for consideration in your model. You can write the multiple linear regression equation for a model ...

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

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 ...

Multiple linear regression is widely used in the real estate industry to estimate housing prices. Instead of relying on a single factor, it considers several variables simultaneously.

One common problem in the use of multiple linear or logistic regression when analysing clinical data is the occurrence of explanatory variables (covariates) which are not independent, ie ...

This article briefly reviews classical suppressor variables, suppression and enhancement, opposing signs of regression coefficients and zero-order correlations, and multicollinearity. A concise and ...

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, ...