Correlation vs Regression: Both correlation and regression are two powerful tools of statistics and data analysis used to understand the relationships between variables.

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

Nonlinear regression is a form of regression analysis in which data fit to a model is expressed as a mathematical function.

Prompted by a connection between MacKinnon and White's HC2 HCCM estimator and the heterogeneous-variance two-sample t statistic, the authors provide a new statistic for testing linear hypotheses in an ...

Polynomial regression with response surface analysis is a sophisticated statistical approach that has become increasingly popular in multisource feedback research (e.g., self-observer rating ...

Linear Regression vs. Multiple Regression: An Overview Linear regression (also called simple regression) is one of the most common techniques of regression analysis. Multiple regression is a ...