Estimate AIC, BIC, RMSE, MAE, and R-squared values. Test fit quality from core summary inputs. Turn raw model outputs into confident evaluation insights today.
| Model | n | p | k | SSE | SST | Log-Likelihood | Mean Response | Null Deviance | Residual Deviance |
|---|---|---|---|---|---|---|---|---|---|
| Customer Churn Model | 120 | 5 | 6 | 84.50 | 215.00 | -52.40 | 18.60 | 168.00 | 101.20 |
| Statistic | Formula |
|---|---|
| MSE | MSE = SSE / (n - p - 1) |
| RMSE | RMSE = √MSE |
| R-squared | R² = 1 - (SSE / SST) |
| Adjusted R-squared | Adjusted R² = 1 - (1 - R²) × ((n - 1) / (n - p - 1)) |
| F Statistic | F = ((SST - SSE) / p) / (SSE / (n - p - 1)) |
| AIC | AIC = 2k - 2LL |
| AICc | AICc = AIC + [2k(k + 1) / (n - k - 1)] |
| BIC | BIC = ln(n) × k - 2LL |
| CVRMSE | CVRMSE = (RMSE / |Mean Response|) × 100 |
| Pseudo R-squared | Pseudo R² = 1 - (Residual Deviance / Null Deviance) |
Model fit statistics help analysts judge whether a statistical model explains data well. A single score rarely tells the whole story. Good evaluation combines error measures, variance measures, likelihood measures, and complexity penalties. This calculator gathers them in one place, making regression review faster and more consistent for students, researchers, and working analysts.
When you assess fit, begin with residual error. SSE shows the unexplained variation left after estimation. MSE converts that residual total into an average scaled by degrees of freedom. RMSE returns the error to the original data unit, which improves interpretation. MAE adds another practical lens because it tracks average absolute miss size without squaring every error.
Variance-based statistics are equally useful. R-squared estimates the share of total variation explained by the model. Adjusted R-squared improves on that measure by penalizing unnecessary predictors. This matters when you compare models with different numbers of inputs. A model can raise raw R-squared slightly while still becoming weaker after adjustment, especially when added variables contribute little signal.
Likelihood-based measures are valuable in model comparison. AIC and BIC both balance fit and complexity, but BIC usually penalizes extra parameters more heavily. Lower values often indicate a better candidate when models are estimated on the same dataset. AICc extends AIC for smaller samples, where overfitting risk increases and standard AIC may look too optimistic.
This calculator also reports the F statistic for overall regression significance and optional deviance improvement for broader model families. Together, these values help you understand whether the model fits well, whether it is too complex, and whether another specification may be more efficient. Use the tool when checking linear regression, comparing candidate equations, documenting model quality, or preparing academic and business reports. Strong interpretation comes from reading the statistics together, not from trusting one metric alone. That broader view supports better decisions, clearer communication, and more reliable statistical modeling. For best use, enter accurate sample size, predictor count, and either likelihood or sums of squares. Then compare several models side by side. The most useful choice usually shows lower information criteria, acceptable error, stable adjusted R-squared, and strong practical interpretability for the decision context across future prediction tasks.
Model fit describes how closely a statistical model matches observed data. Better fit usually means smaller errors, sensible complexity, and stronger explanatory power.
No. A higher R-squared can come from adding weak predictors. Adjusted R-squared helps show whether the extra variables actually improve the model.
Use them when comparing models fitted to the same dataset. Lower values usually indicate a better balance between fit quality and model complexity.
RMSE expresses error in the original response unit. That makes it easier to understand practical prediction accuracy and compare it with real-world tolerances.
This usually means the error degrees of freedom are not positive. Check that your sample size is larger than the predictor count plus one.
Yes, for likelihood and deviance-based measures such as AIC, BIC, deviance improvement, and pseudo R-squared. Standard SSE and R-squared are mainly for linear settings.
No. Strong evaluation comes from reading several metrics together. Pair error measures with information criteria and model purpose before making a final choice.
CVRMSE scales RMSE by the mean response. It helps compare model error across datasets or systems with very different measurement magnitudes.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.