Regression analysis is a statistical method used to study the relationship between a dependent variable and one or more independent variables.

One of the most commonly used methods for linear regression analysis is R-Squared.

In this article, you'll get to know what R-Squared is and the meaning of its value(s). You'll also see some of the fields where it is used.

What is R Squared?

R-Squared (R²) is a statistical measure used to determine the proportion of variance in a dependent variable that can be predicted or explained by an independent variable.

In other words, R-Squared shows how well a regression model (independent variable) predicts the outcome of observed data (dependent variable).

R-Squared is also commonly known as the coefficient of determination. It is a goodness of fit model for linear regression analysis.

What Does an R Squared Value Mean?

An R-Squared value shows how well the model predicts the outcome of the dependent variable. R-Squared values range from 0 to 1.

An R-Squared value of 0 means that the model explains or predicts 0% of the relationship between the dependent and independent variables.

A value of 1 indicates that the model predicts 100% of the relationship, and a value of 0.5 indicates that the model predicts 50%, and so on.

The formula below is mostly used to find the value of R-Squared:

R² = 1 - RSS/TSS


  • R² = coefficient of determination
  • RSS = sum of squares of residuals
  • TSS = total sum of squares

Where Is R Squared Used?

R-Squared is used by different fields. It can be used for the following:

  • Risk analysis in finance.
  • Marketing campaigns.
  • Scientific research.
  • Economics.
  • Sports analysis.


In this article, we talked about R-Squared. It is a statistical method mostly used in predicting the outcome of data.

We started by looking at what R-Squared means. We then talked about the meaning of its value and how to calculate it.

Lastly, we talked about the different fields where R-Sqaured can be used.

Thank you for reading!