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

Although MLR is similar to linear regression, the interpretation of MLR correlation coefficients is confounded by the way in which the predictor variables relate to one another.

In this article we consider the linear quantile regression model with a power transformation on the dependent variable. Like the classical Box—Cox transformation approach, it extends the applicability ...

Multiple linear regression (MLR) is a statistical technique that uses several explanatory variables to predict the outcome of a response variable.

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 ...

Course TopicsLinear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the ...

A linear regression is a statistical model that attempts to show the relationship between two variables with a linear equation. A regression analysis involves graphing a line over a set of data ...

The models for the response variable consist of a linear effect composed of the exogenous covariables together with a random disturbance term. The distribution of the random disturbance can be taken ...