Data Source and Methodology

All calculations are based on standard statistical formulas for computing the coefficient of determination. Visit this source for more details. All calculations are strictly based on the formulas and data provided by this source.

The Formula Explained

The R-squared value is calculated as follows: \[ R^2 = 1 - \frac{\sum{(y_i - f_i)^2}}{\sum{(y_i - \bar{y})^2}} \]

Glossary of Variables

X values: Independent variable data points.
Y values: Dependent variable data points.
R-squared: The proportion of variance in the dependent variable predictable from the independent variable(s).

How It Works: A Step-by-Step Example

To calculate the R-squared value, input your independent (X) and dependent (Y) variables. The calculator applies the formula to determine the coefficient of determination, which reflects how well the data fits a statistical model.

Frequently Asked Questions (FAQ)

What does an R-squared value indicate?

R-squared indicates the proportion of the variance in the dependent variable that is predictable from the independent variable(s).

Can R-squared be negative?

R-squared values usually range from 0 to 1. A negative value would indicate that the model is worse than a horizontal line model.

Is a higher R-squared always better?

A higher R-squared indicates a better fit, but it does not necessarily indicate a correct model. Model selection should involve additional criteria.

Tool developed by Ugo Candido. Content reviewed by the CalcDomain Expert Team.
Last reviewed for accuracy on: October 15, 2023.