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R squared of a linear regression

by Marco Taboga, PhD

How good is a linear regression model in predicting the output variable on the basis of the input variables?

How much of the variability in the output is explained by the variability in the inputs of a linear regression?

The R squared of a linear regression is a statistic that provides a quantitative answer to these questions.

$The R squared of a linear regression is the fraction of total variance explained by the regression.$

Table of contents

Caveat
The linear regression model
Sample variance of the outputs
Sample variance of the residuals
Definition of R squared
Properties and interpretation
Alternative definition
Adjusted R squared
Interpretation of the adjusted R squared
More details about the degrees-of-freedom adjustment

Caveat

Before defining the R squared of a linear regression, we warn our readers that several slightly different definitions can be found in the literature.

Usually, these definitions are equivalent in the special, but important case in which the linear regression includes a constant among its regressors.

We choose a definition that is easy to understand, and then we make some brief comments about other definitions.

The linear regression model

Consider the linear regression modelwhere $x_{i}$ is a vector of inputs and is a vector of regression coefficients.

Suppose that we have a sample of observations , for .

Given an estimate of (for example, an OLS estimate), we compute the residuals of the regression:

Sample variance of the outputs

Denote by $S_{y}^{2}$ the unadjusted sample variance of the outputs: [eq4] where is the sample mean [eq5]

The sample variance $S_{y}^{2}$ is a measure of the variability of the outputs, that is, of the variability that we are trying to explain with the regression model.

Sample variance of the residuals

Denote by $S_{e}^{2}$ the mean of the squared residuals: [eq6] which coincides with the unadjusted sample variance of the residuals when the sample mean of the residuals [eq7] is equal to zero.

Unless stated otherwise, we are going to maintain the assumption that in what follows.

The sample variance $S_{e}^{2}$ is a measure of the variability of the residuals, that is, of the part of the variability of the outputs that we are not able to explain with the regression model.

Intuitively, when the predictions of the linear regression model are perfect, then the residuals are always equal to zero and their sample variance is also equal to zero.

On the contrary, the less the predictions of the linear regression model are accurate, the highest the variance of the residuals is.

Definition of R squared

We are now ready to give a definition of R squared.

Definition The R squared of the linear regression, denoted by $R^{2}$ , is [eq8] where $S_{e}^{2}$ is the sample variance of the residuals and $S_{y}^{2}$ is the sample variance of the outputs.

Thus, the R squared is a decreasing function of the sample variance of the residuals: the higher the sample variance of the residuals is, the smaller the R squared is.

Properties and interpretation

Note that the R squared cannot be larger than 1: it is equal to 1 when the sample variance of the residuals is zero, and it is smaller than 1 when the sample variance of the residuals is strictly positive.

The R squared is equal to 0 when the variance of the residuals is equal to the variance of the outputs, that is, when predicting the outputs with the regression model is no better than using the sample mean of the outputs as a prediction.

It is possible to prove that the R squared cannot be smaller than 0 if the regression includes a constant among its regressors and is the OLS estimate of (in this case we also have that ). Outside this important special case, the R squared can take negative values.

In summary, the R square is a measure of how well the linear regression fits the data (in more technical terms, it is a goodness-of-fit measure): when it is equal to 1 (and ), it indicates that the fit of the regression is perfect; and the smaller it is, the worse the fit of the regression is.

Alternative definition

Another common definition of the R squared is [eq9]

This definition is equivalent to the previous definition in the case in which the sample mean of the residuals $e_{i}$ is equal to zero (e.g., if the regression includes an intercept).

Check the Wikipedia article for other definitions.

Adjusted R squared

The adjusted R squared is obtained by using the adjusted sample variances [eq10] and [eq11] instead of the unadjusted sample variances $S_{y}^{2}$ and $S_{e}^{2}$ .

This is done because $s_{y}^{2}$ and $s_{e}^{2}$ are unbiased estimators of and under certain assumptions (see the lectures on Variance estimation and The Normal Linear Regression Model).

Definition The adjusted R squared of the linear regression, denoted by $overline{R}^{2}$ , is [eq14] where $s_{e}^{2}$ is the adjusted sample variance of the residuals and $s_{y}^{2}$ is the adjusted sample variance of the outputs.

The adjusted R squared can also be written as a function of the unadjusted sample variances: [eq15]

Proof

This is an immediate consequence of the fact thatand

The ratioused in the formula above is often called a degrees-of-freedom adjustment.

Interpretation of the adjusted R squared

The intuition behind the adjustment is as follows.

When the number of regressors is large, the mere fact of being able to adjust many regression coefficients allows us to significantly reduce the variance of the residuals. As a consequence, the R squared tends to be small.

This phenomenon is known as overfitting. The extreme case is when the number of regressors is equal to the number of observations and we can choose so as to make all the residuals equal to .

But being able to mechanically make the variance of the residuals small by adjusting does not mean that the variance of the errors of the regression $arepsilon _{i}$ is as small.

The degrees-of-freedom adjustment allows us to take this fact into consideration and to avoid under-estimating the variance of the error terms.

More details about the degrees-of-freedom adjustment

In more technical terms, the idea behind the adjustment is that what we would really like to know is the quantity [eq19] but the unadjusted sample variances $S_{e}^{2}$ and $S_{y}^{2}$ are biased estimators of and .

The bias is downwards, that is, they tend to underestimate their population counterparts.

As a consequence, we estimate and with the adjusted sample variances $s_{e}^{2}$ and $s_{y}^{2}$ , which are unbiased estimators.

How to cite

Please cite as:

Taboga, Marco (2021). "R squared of a linear regression", Lectures on probability theory and mathematical statistics. Kindle Direct Publishing. Online appendix. https://www.statlect.com/fundamentals-of-statistics/R-squared-of-a-linear-regression.