Why This Regression Test Matters
An F-test using R-squared checks whether a regression model explains meaningful variation. It compares explained variance with unexplained variance. The result helps you decide whether the fitted predictors work better than a model with no predictors. This calculator turns R-squared, sample size, and predictor count into a complete test summary.
When To Use It
Use this method for the overall significance test in multiple regression. It is useful after you build a model and know its R-squared value. The test asks one direct question. Do the predictors, taken together, explain enough variation to be statistically useful? It is not a test for one single coefficient. For individual terms, use t tests or partial F tests.
What The Inputs Mean
R-squared shows the proportion of outcome variance explained by the model. The predictor count is the number of independent variables, not including the intercept. Sample size is the number of observations used to fit the model. The alpha level sets your decision threshold. Common choices are 0.10, 0.05, and 0.01.
How To Read The Output
A larger F statistic usually gives stronger evidence against the null model. The p-value estimates the probability of seeing such a strong model fit, assuming the predictors have no real combined effect. If the p-value is less than alpha, the model is significant. If it is higher, the result is not significant.
Practical Notes
High R-squared alone is not enough. Check residual plots, outliers, collinearity, and model assumptions. Large samples can make small effects significant. Small samples can hide useful effects. Always compare statistical significance with practical value. Also confirm that the response variable and predictors match the research question.
Reporting The Result
A clear report includes F value, numerator degrees of freedom, denominator degrees of freedom, p-value, R-squared, adjusted R-squared, and the decision rule. You can export the table as CSV or PDF. Keep the exported file with your model notes, data source, and assumption checks for later review.
Limitations
This calculator assumes an ordinary least squares setting. It also assumes the reported R-squared came from the same sample size and predictor count. Rounded inputs can slightly change the final p-value. Use original model output when possible.