In statistics, R-squared (R2) is a statistical measure that represents the proportion of the variance for a dependent variable thats explained by an independent variable in a regression model. It is a measure of how well the data fit the regression model, also known as the goodness of fit. R-squared can take any values between 0 to 1, and a higher R-squared indicates more variability is explained by the model. The calculation of R-squared requires several steps, including finding the line of best fit, calculating predicted values, subtracting actual values, and squaring the results to yield a list of errors squared, which is then summed and equals the unexplained variance. R-squared is always between 0 and 100% .

R-squared is commonly used in investing to interpret the percentage of a fund’s or security’s price movements that can be explained by movements in a benchmark index. For example, an R-squared for a fixed-income security vs. a bond index identifies the security’s proportion of price movement that is predictable based on a price movement of the index. The same can be applied to a stock vs. the S&P 500 Index or any other relevant index.

It is important to note that R-squared does not disclose information about the causation relationship between the independent and dependent variables, nor does it indicate the correctness of the regression model. Therefore, it is recommended to analyze R-squared together with the other variables in a statistical model.