# How to Calculate R-Squared in Google Sheets

R-squared, often written as r2, 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 r2 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?