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Multiple Regression: A Short Introduction from Leanscape

Multiple regression is a statistical technique that is used to predict the dependent variable based on one or more independent variables. In other words, it is used to determine how well a certain explanatory variable can explain the variation in the dependent variable.

Multiple regression can be used for both linear and nonlinear relationships. The technique is also known as “multiple linear regression” when only linear relationships are considered.

This post will briefly introduce multiple regression, including when and how it should be used.

What is Multiple Regression?

 

As mentioned previously, multiple regression is a statistical technique that is used to predict the dependent variable based on one or more independent variables. In other words, it is used to determine how well a certain explanatory variable can explain the variation in the dependent variable.

For example, let’s say you want to use multiple regression to predict someone’s annual income based on their age, education level, and number of years of experience. The dependent variable would be annual income, while the independent variables would be age, education level, and number of years of experience.

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How is Multiple Regression Used?

 

Multiple regression can be used for both linear and nonlinear relationships. The technique is also known as “multiple linear regression” when only linear relationships are considered.

Linear relationships are straight-line relationships between two variables, while nonlinear relationships are curvilinear relationships between two variables.

For example, let’s say you want to use multiple linear regression to predict someone’s annual income based on their age and education level. The relationship between annual income and age would be linear (a straight line), while the relationship between annual income and education level would also be linear (a straight line).

However, if you wanted to use multiple regression to predict someone’s weight based on their height, the relationship between weight and height would be nonlinear (a curvilinear relationship).

How Does Multiple Regression Work?

 

Multiple regression uses multiple independent variables to predict a single dependent variable. The independent variables are also predictor variables, and the dependent variable is the criterion variable.

There are two types of multiple regression: linear and nonlinear. Linear multiple regression models the relationships between the predictor and criterion variables as straight lines. Nonlinear multiple regression models the relationships between the predictor and criterion variables as curvilinear relationships.

Both types of multiple regression require that the data meets certain assumptions, such as normality, linearity, homoscedasticity, and independence. These assumptions must be met for the results of the multiple regression to be valid.

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When Should Multiple Regression Be Used?

 

There are several circumstances in which multiple regression should be used:

• To predict a continuous outcome variable from a set of predictor variables

• When there is more than one predictor variable

• When there are no outliers in the data

• When there is little or no multicollinearity in the data

• When there is no significant non-linearity in the data


Multiple Regression Analysis

 

 

Conclusion:

 

Multiple regression is a statistical technique that can predict the dependent variable based on one or more independent variables. The technique can be used for both linear and nonlinear relationships. Multiple regression should be used when there is more than one predictor variable, when there are no outliers in the data, when there is little or no multicollinearity, and when there is no significant non-linearity.

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