Multiple Regression Calculator
Evaluate a multiple regression model using SSR, SSE, number of predictors, and sample size. Calculate R², adjusted R², F-statistic, and model fit metrics.
Inputs
Results
R-Squared (R²)
0.75
Adjusted R-Squared
0.73
F-Statistic
46
How to Use This Calculator
- Enter Regression Sum of Squares (SSR), Error Sum of Squares (SSE), and Number of Predictors (k).
- Set Sample Size (n).
- Review R-Squared (R²), Adjusted R-Squared, and F-Statistic.
- Use P-Value (approx) and Root Mean Square Error to inform your decision.
- Use the chart to visualize the results and explore different scenarios by adjusting inputs.
How the result changes with Regression Sum of Squares (SSR)
| Regression Sum of Squares (SSR) | R-Squared (R²) | Adjusted R-Squared | F-Statistic |
|---|---|---|---|
| 100,000,000,000 | 1 | 1 | 1,022,222,222.22 |
| 350,000,000,000 | 1 | 1 | 3,577,777,777.78 |
| 650,000,000,000 | 1 | 1 | 6,644,444,444.44 |
| 900,000,000,000 | 1 | 1 | 9,200,000,000 |
What each input means
- Regression Sum of Squares (SSR)
- Sum of squares explained by the regression model.
- Error Sum of Squares (SSE)
- Sum of squares not explained by the model (residual error).
- Number of Predictors (k)
- Number of independent variables in the regression model.
- Sample Size (n)
- Total number of observations. Must exceed the number of predictors + 1.
How this is calculated
Worked example, using the default values
- Identify Input Parameters4 parametersRegression Sum of Squares (SSR) = 4500, Error Sum of Squares (SSE) = 1500, Number of Predictors (k) = 3, Sample Size (n) = 50 = 4 input(s) provided
- Calculate R-SquaredR-Squared0.75 = 0.75
- Calculate Adjusted R-SquaredAdjusted R-Squared0.7337 = 0.7337
- Calculate F-StatisticF-Statistic46 = 46
- Calculate P-ValueP-Value0 = 0
- Calculate Root Mean Square ErrorRoot Mean Square Error5.7104 = 5.7104
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