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.

Multiple Linear Regression: Multiple linear regression describes the correlation between two or more independent variables and a dependent variable, also using a straight regression line.

Linear regression is a powerful and long-established statistical tool that is commonly used across applied sciences, economics and many other fields. Linear regression considers the relationship ...

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 extension of Lesson 9. I will start with a ...

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 ...

In logistics regression, you can use machine learning to help predict the probability of the outcome of a situation with two potentials. For instance, it is good for predicting whether something ...

Max Halperin, Joan Gurian, Confidence Bands in Linear Regression with Constraints on the Independent Variables, Journal of the American Statistical Association, Vol. 63, No. 323 (Sep., 1968), pp. 1020 ...

Relative importance of regressor variables is an old topic that still awaits a satisfactory solution. When interest is in attributing importance in linear regression, averaging over orderings methods ...