**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.

The following example shows how to calculate R-squared for two variables in Google Sheets.

**Example: Calculating R-Squared in Google Sheets**

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

Now suppose we want to fit a simple linear regression model, using “hours” as the predictor variable and “score” as the response variable.

To find the R-squared for this model, we can use the **RSQ()** function in Google Sheets, 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

In our example, we can type the following formula into cell D2:

=RSQ(A2:A21, B2:B21)

The following screenshot shows how to use this formula in practice:

The R-squared value turns out to be about **0.7273**.

This means that **72.73%** of the variation in the exam scores can be explained by the number of hours studied.

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

**Additional Resources**

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

How to Find A Line of Best Fit in Google Sheets

How to Perform Linear Regression in Google Sheets

How to Create a Forecast in Google Sheets (With Example)