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

In technical terms, it is 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?

This tutorial explains how to calculate r^{2} for two variables in Excel.

**Example: Calculating R-Squared in Excel**

Suppose we have the following data for the number of hours studied and the exam score received for 20 students:

Now suppose we are interested in fitting a simple linear regression model to this data, using “hours” as the predictor variable and “score” as the response variable.

To find the r^{2} for this data, we can use the RSQ() function in Excel, which uses the following syntax:

**=RSQ(known_ys, known_xs)**

where:

**known_ys:**the values for the response variable**known_xs:**the values for the predictor variable

Here’s what that formula looks like in our example:

In this example, **72.73%** of the variation in the exam scores can be explained by the number of hours studied.

Note that if we fit a simple linear regression model to this data, the output would look like this:

Notice that the R Square value in the first table is **0.7273**, which matches the result that we got using the RSQ() function.