The R-squared statistic quantifies the predictive accuracy of a statistical model. It shows the proportion of variance in the outcome variable that is explained by the predictions. It is also known as the coefficient of determination, R², r², and r-square. This article will go over the key properties of R², how it is computed and its limitations.

## Key properties of R-squared

*R-squared, otherwise known as R²* typically has a value in the range of 0 through to 1. A value of 1 indicates that predictions are identical to the observed values; it is not possible to have a value of *R² *of more than 1. A value of 0 indicates that there is no linear relationship between the observed and predicted values, where “linear” in this context means that it is still possible that there is a non-linear relationship between the observed and predicted values. Finally, a value of 0.5 means that half of the variance in the outcome variable is explained by the model. Sometimes the *R² *is presented as a percentage (e.g., 50%).

## How is the R-squared statistic computed?

There are many equivalent ways of computing *R**². *Perhaps the simplest is:

*R²* = Explained sum-of-squares / Total sum-of-squares

I've illustrated this in the table below. The first column, called Observed, shows the nine observed values (i.e., of the *outcome variable*). The second column contains the observed values minus their average value of 1.95. The third column squares these values. The sum of these squared values is called the *Total sum-of-squares (TSS). *

The fourth column shows the predicted values (in this case from a *linear regression*). The *Explained-sum-of-squares *are computed using the predicted values in the same way as was done with the observed values. The ratio of these numbers, 0.18909/3.27 = 0.05783 = *R².*

An alternative way of computing *R² *is as the square of *Pearson’s product-moment correlation. *In most conventional situations these two calculations will produce the same values. They can differ when the model being used is not sensible (e.g., a model where the predictions are less accurate than chance) or when being computed for data that has not been used when fitting the model.

## How to use R^{2}

*R² *has two main uses. One is to provide a basic summary of how well a model fits the data. If *R² *is only 0.1, then in an absolute sense the *R² *is only explaining a tenth of what can be explained. Similarly, an *R² *of .99 is explaining almost all that can be explained.

The other main application of *R² *is to compare models. All else being equal, a model with a higher *R² *is a better model.

## Limitations of R-squared

A common misunderstanding of *R² *is that there is a threshold. For example, a model needs to have an *R² *of more than 0.9 to be good. This is rarely true: for example, a model that predicts that future share prices may be able to earn billions of dollars in profits for a hedge fund even if the *R² *is only 0.01.

*R² *can is also problematic when comparing models. In the case of regression, for example, if you add an extra predictor the *R² *will almost always increase. Therefore, while it is common for researchers to have a look at *R² *when comparing models, more sophisticated methods (e.g., statistical tests, information criteria) should be used most of the time.

## Variants of R^{2}

There are a number of variants of *R². *The most well-known is the *Adjusted R² Statistic, *which is designed to make it possible to compare *R² *across models with different numbers of predictors. Various *pseudo-R² *statistics have been developed for models for categorical outcome variables.

**Make sure you check out our post on "8 tips for interpreting R-Squared"! ****Got a term you're not sure about it? Check out more of our "What is" guides. **