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

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 ...

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 ...

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 ...

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