Regression and correlation analysis are both useful methods of data analysis that can help you to understand how multiple dependent and independent variables relate to each other.

Learn how to interpret multiple linear regression with more than two independent variables, and what to look for in the output. Find out how to check assumptions, read coefficients, measure fit ...

Multiple regression models add additional dimensions of independent variables, each with their own slopes. This can be helpful for identifying confounding variables and avoiding spurious associations, ...

To perform a regression analysis, a statistician collects a set of data points, each including a complete set of dependent and independent variables. For example, the dependent variable could be a ...

It incorporates multiple independent variables. For example, multiple regression could capture how the daily change in a company's stock price is impacted by the company's P/E ratio, dividends ...

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 ...