This article describes how to get an Adjusted R-Square in Linear Regression. The Adjusted R-Square is a modification of the R-Square statistic which takes into account the number of variables in the model (i.e., more variables translates to a lower R-Square if the extra variables do not explain a non-trivial amount of the variance of the dependent variable).

Use of the Adjusted R-Square statistic is not considered good practice when comparing models and Information Criteria are generally recommended to be used instead of the Adjusted R-Square statistic.

## Requirements

*Predictor variables*(aka*features*or*independent variables*) - these can be numeric or binary. To use categorical variables in regression, you need to create a separate dummy variable for each category and use those instead (e.g. if Employment Category has three categories (manager, custodial, clerical) you can create three new variables called manager, custodial and clerical)- An
*outcome variable*(aka*dependent variable*) - this variable must be*numeric*.

## Method

- From the
**toolbar**, go to**Anything > Advanced Analysis > Regression > Linear Regression** - In the
**object inspector,**select your numeric**Outcome**variable. - In
**Predictor(s),**select your predictor variable(s). The fastest way to do this is to select them all in the**Data Sets**tree and drag them into the**Predictor(s)**box, but if you are using dummy variables you created from a categorical variable, be sure to leave one out to serve as the reference category. - From
**Algorithm**, choose**Regression** - From
**Regression type**, choose**Linear**. - From
**Output,**choose**Detail**.

The Adjusted R-Squared is slightly less than the Multiple R-squared. For this reason, it is often considered a more conservative estimate than the Multiple R-Squared for the amount of explained variance.

## See Also

How to Run Linear Regression in Displayr

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