This article describes how to view the Adjusted R-Square value in Linear Regression. The Adjusted R-Square is a modification of the R-Square statistic that takes into account the number of variables in the model (i.e., more variables translate 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, categorical, or binary. If using a categorical (Nominal) variable, the output will create dummy variables and include the first category in the base model automatically.
- An outcome variable (aka dependent variable) - this variable must be numeric.
Method
From either the + in the Report tree or via Anything
in the toolbar, go to Advanced Analysis > Regression > Linear Regression.
In Properties
, 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 Sources tree and drag them into the Predictor(s) box.
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.