Calculate R-Squared in Excel: Step-by-Step Guide and Common Mistakes

What Is R-Squared?

R-squared is a statistical measure in the financial world that represents the percentage of a fund's or a security's movements that can be explained by movements in a benchmark index. R-squared typically ranges from 1% to 100% in this field. Excel can help analysts and investors calculate R-squared in an automated fashion. Relevant Excel functionalities for this calculation include RSQ and CORREL.

R-squared uses the variance of one variable to explain the variance of another. Additional testing is required to determine if R-squared approaching +/- 1 is statistically significant. When calculating R-squared, it's important to understand that variables must be independent and their relationship must be linear for a correlation to exist. Data must be normalized into a common unit. And you would theoretically test these claims to determine if a correlation calculation is appropriate.

Understanding the R-Squared Formula

$\begin{aligned}&R^2=1-\frac{RSS}{TSS}\\&\textbf{where:}\\&R^2=\text{Coefficient of determination}\\&RSS=\text{Sum of squares of residuals}\\&TSS=\text{Total sum of squares}\end{aligned}$​R2=1−TSSRSS​where:R2=Coefficient of determinationRSS=Sum of squares of residualsTSS=Total sum of squares​

Avoiding Common R-Squared Errors

The most common mistake made with R-squared is assuming that an R-squared approaching +/- 1 is statistically significant. A reading approaching +/- 1 increases the chances of actual statistical significance but it's impossible to know based on the result alone without further testing.

Statistical testing isn't completely straightforward. It can get complicated for several reasons. A critical assumption of correlation and R-squared is that the variables are independent and the relationship between them is linear. In theory, you would test these claims to determine if a correlation calculation is appropriate.

The second most common mistake is forgetting to normalize the data into a common unit.

The units are already normalized if you're calculating a correlation or R-squared on two betas. The unit is beta. It's critical that you normalize them into percent returns and not share price changes if you want to correlate stocks. This happens frequently, even among investment professionals. 

You're essentially asking two questions for stock price correlation or R-squared: What is the return over a certain number of periods, and how does that variance relate to another securities variance over the same period? Two securities might have a high correlation or R-squared if the return is daily percent changes over the past 52 weeks. They could have a low correlation if the return is monthly changes over the past 52 weeks.

Which is better? It depends on the purpose of the test.

Step-by-Step Guide to Calculating R-Squared in Excel

1. Gather Your Data: Compile the two data sets you want to compare. These will usually be arranged in columns.
 
2. Use the RSQ Function: Enter the formula `=RSQ([Data set 1], [Data set 2])` in an empty cell. Replace `[Data set 1]` and `[Data set 2]` with the actual data range references.
 
3. Double-Check Your Inputs: Make sure your data ranges are correct to ensure accurate results.

You can calculate R-squared in Excel using several methods. The simplest way is to get two data sets and use the built-in R-squared formula. Another alternative is to find a correlation and then square it. Both are shown here:

The Bottom Line

R-squared is a statistical measure that explains the variance of one variable using the variance of another. The initial result of the calculation isn’t necessarily significant but further testing can corroborate it. Investors can normalize their data into percent returns to correlate stocks.