R-squared Calculator: Coefficient of Determination
Easily calculate the R-squared value to understand the goodness of fit for your regression model. Input your data and predicted values for instant results and insightful visualizations.
Data Inputs
Enter your data sets to calculate the R-squared value. Ensure both datasets are comma-separated and of equal length.
Comma-separated numerical values.
Comma-separated predicted numerical values.
Result
R-squared Value:
The R-squared value, also known as the coefficient of determination, ranges from 0 to 1. It represents the proportion of the variance in the dependent variable that is predictable from the independent variable(s).
- R-squared = 1: Perfect fit. The model explains all the variance in the dependent variable.
- R-squared = 0: No fit. The model explains none of the variance.
- 0 < R-squared < 1: Indicates the extent to which the variance in the dependent variable is predictable from the independent variable(s).
Understanding R-squared
R-squared, or the coefficient of determination, is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model. In simpler terms, it shows how well the data points fit a regression line.
It's a crucial metric in evaluating the performance of regression models. A higher R-squared value generally indicates a better fit, suggesting that the model is effective at predicting the dependent variable. However, R-squared doesn\'t tell the whole story and should be considered alongside other metrics and domain knowledge.
For further reading, you can explore resources like: