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

When multiple variables are associated with a response, the interpretation of a prediction equation is seldom simple.

Multiple regression analysis is one of the most widely used statistical procedures for both scholarly and applied marketing research. Yet, correlated predictor variables-and potential collinearity ...

The effectiveness of various analytical formulas for estimating R² shrinkage in multiple regression analysis was investigated. Two categories of formulas were identified: estimators of the squared ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...