**R-squared**, often written as r^{2}, is a measure of how well a linear regression model fits a dataset.

This value represents the proportion of the variance in the response variable that can be explained by the predictor variable.

The value for r^{2} can range from 0 to 1:

- A value of 0 indicates that the response variable cannot be explained by the predictor variable at all.
- A value of 1 indicates that the response variable can be perfectly explained without error by the predictor variable.

**Related: **What is a Good R-squared Value?

The following step-by-step example shows how to calculate the R-squared value for a simple linear regression model in SAS.

**Step 1: Create the Data**

For this example, we’ll create a dataset that contains the total hours studied and final exam score for 15 students.

We’ll to fit a simple linear regression model using *hours* as the predictor variable and *score* as the response variable.

The following code shows how to create this dataset in SAS:

/*create dataset*/ data exam_data; input hours score; datalines; 1 64 2 66 4 76 5 73 5 74 6 81 6 83 7 82 8 80 10 88 11 84 11 82 12 91 12 93 14 89 ; run; /*view dataset*/ proc print data=exam_data;

**Step 2: Fit the Simple Linear Regression Model**

Next, we’ll use **proc reg** to fit the simple linear regression model:

/*fit simple linear regression model*/ proc reg data=exam_data; model score = hours; run;

Notice that the R-squared value in the output is 0.8310.

This means **83.1%** of the variation in exam scores can be explained by the number of hours studied.

**Step 3: Extract R-Squared Value of Regression Model**

If you only want to view the R-squared value of this model and none of the other output results, you can use the following code:

/*fit simple linear regression model*/ proc reg data=exam_data outest=outest noprint; model score = hours / rsquare; run; quit; /*print R-squared value of model*/ proc print data=outest; var _RSQ_; run;

Notice that only the R-squared value of **0.83098** is shown in the output.

**Note**: The argument **noprint** in** proc reg** tells SAS not to print the entire output of regression results as it did in the previous step.

**Additional Resources**

The following tutorials explain how to perform other common tasks in SAS:

How to Perform Simple Linear Regression in SAS

How to Perform Multiple Linear Regression in SAS

How to Perform Polynomial Regression in SAS

How to Perform Logistic Regression in SAS