**Definition:** Multiple regression analysis is a statistical method used to predict the value a dependent variable based on the values of two or more independent variables.

## What Does Multiple Regression Analysis Mean?

**What is the definition of multiple regression analysis?** The value being predicted is termed dependent variable because its outcome or value depends on the behavior of other variables. The independent variables’ value is usually ascertained from the population or sample.

In business, sales managers use multiple regression analysis to analyze the impact of some promotional activities on sales. Multiple regression analysis can be used to also unearth the impact of salary increment and increments in other employee benefits on employee output. The analysis is useful when you want to predict the impact of individual independent variables on the desired outcome.

This analysis makes some assumptions on the margin of error for the analysis, which needs to be checked when using the model. The most common is that, the errors are independent and normally distributed. It also assumes the errors have constant variance and the mean of the errors is zero.

Let’s look at an example.

## Example

Multiple regression analysis can be performed using Microsoft Excel and IBM’s SPSS. Other statistical tools can equally be used to easily predict the outcome of a dependent variable from the behavior of two or more independent variables.

This analysis can be used to predict how well a new process in a company is responding to some tweaks made to that process. It can also be used at home to ascertain changes in the cost of energy consumed based on some energy conservation methods and equipment employed in the house. In schools, this analysis is used to determine the performance of students using class hours, library hours, and leisure hours as the independent variables.

## Summary Definition

**Define Multiple Regression Analysis:** MRA means a method of predicting outcomes based on manipulating one variable at a time.