Advanced R2 Fit Calculator

Analyze regression fit with clean inputs and summaries. See diagnostics, charts, and export-ready performance details. Make smarter modeling decisions with reliable fit evidence today.

Enter R² Fit Inputs

Use comma, space, or line breaks between values. The calculator compares each actual value with its matching prediction.

Formula Used

Coefficient of Determination:
R² = 1 - (SSE / SST)
Residual Sum of Squares:
SSE = Σ(yi - ŷi)2
Total Sum of Squares:
SST = Σ(yi - ȳ)2
Adjusted R²:
Adjusted R² = 1 - [(1 - R²)(n - 1) / (n - p - 1)]

Here, yi is the actual value, ŷi is the predicted value, ȳ is the mean of actual values, n is the number of observations, and p is the number of predictors.

R² shows how much variability in the actual data is explained by the predictions. Adjusted R² penalizes unnecessary predictors.

How to Use This Calculator

  1. Enter a model label for easier report identification.
  2. Paste actual values into the first box.
  3. Paste predicted values into the second box in matching order.
  4. Enter the number of predictors for adjusted R².
  5. Choose decimal precision for displayed metrics.
  6. Click Calculate R² Fit to generate the result block above the form.
  7. Review the metrics, interpretation, chart, and residual table.
  8. Use the CSV and PDF buttons to export your findings.

Example Data Table

# Actual Predicted Residual
11201182
2135140-5
31501482
4165170-5
51801764
6210214-4
72402364
8260266-6

Frequently Asked Questions

1. What does R² measure?

R² measures how much of the variation in actual values is explained by your predictions. Higher values usually indicate a better fit, but strong R² alone does not guarantee a reliable model.

2. Can R² be negative?

Yes. A negative R² means the model performs worse than a simple baseline that always predicts the mean of the actual values. This usually signals poor fit or incorrect predictions.

3. Why is adjusted R² useful?

Adjusted R² accounts for the number of predictors. It helps prevent misleading optimism when you add extra variables that do not truly improve model quality.

4. What happens if actual values do not vary?

If all actual values are identical, SST becomes zero. In that case, R² is not generally informative. This calculator marks it as undefined unless the predictions are perfectly identical too.

5. Should I use R² alone to judge a model?

No. Pair R² with RMSE, MAE, residual patterns, domain knowledge, and validation results. A model can show a strong R² while still making practically harmful errors.

6. What does the chart show?

The Plotly graph compares actual values against predicted values and adds a perfect-fit reference line. Points close to that line indicate stronger agreement between observations and predictions.

7. Do actual and predicted inputs need matching lengths?

Yes. Each actual value must align with one predicted value in the same position. The calculator validates this because mismatched pairs would make the fit statistics meaningless.

8. What export options are included?

You can download a CSV file containing summary metrics and row-level data. You can also generate a PDF snapshot of the calculated results for sharing or documentation.

Related Calculators

Model Fit ScoreRegression R SquaredAdjusted Model FitMultiple R SquaredExplained Variance ScoreRegression Fit IndexModel Accuracy ScoreLinear Model FitRegression Performance ScoreR Squared Online

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.