Enter the independent and dependent variables in the tool, and the calculator will determine the residual value.
Related
The residual calculator determines the difference between observed (Y) and predicted values in a linear regression model. The online residual point calculator can evaluate the error in regression analysis.
A regression residual is the difference between an actual value and its predicted value in a regression model. Residual calculators help assess the accuracy of predictions and determine the margin of error for the dataset.
The formula for a residual is:
Residual = Observed value − Predicted value
Where: Observed value = actual measurement of Y, Predicted value = value estimated by the regression model. Residuals also help identify variance and drift in the data.
Consider independent variables X = 1, 13, 5, 7, 9 and dependent variables Y = 2, 4, 6, 18, 10. The residuals for each observation are calculated as follows:
| Obs. | X | Y |
|---|---|---|
| 1 | 1 | 2 |
| 2 | 13 | 4 |
| 3 | 5 | 6 |
| 4 | 7 | 18 |
| 5 | 9 | 10 |
| Obs. | X | Y | X² | Y² | X·Y |
|---|---|---|---|---|---|
| 1 | 1 | 2 | 1 | 4 | 2 |
| 2 | 13 | 4 | 169 | 16 | 52 |
| 3 | 5 | 6 | 25 | 36 | 30 |
| 4 | 7 | 18 | 49 | 324 | 126 |
| 5 | 9 | 10 | 81 | 100 | 90 |
| Sum | 35 | 40 | 325 | 480 | 300 |
Sums of squares:
SSXX = 325 − (35² / 5) = 80
SSYY = 480 − (40² / 5) = 160
SSXY = 300 − (35*40 / 5) = 20
Regression coefficients:
Slope: β̂₁ = SSXY / SSXX = 20 / 80 = 0.25
Intercept: β̂₀ = Ȳ − β̂₁·X̄ = 6.25
Regression equation: Ŷ = 6.25 + 0.25X
| Obs. | X | Y | Predicted Ŷ | Residual (Y − Ŷ) |
|---|---|---|---|---|
| 1 | 1 | 2 | 6.25 + 0.25*1 = 6.5 | 2 − 6.5 = −4.5 |
| 2 | 13 | 4 | 6.25 + 0.25*13 = 9.5 | 4 − 9.5 = −5.5 |
| 3 | 5 | 6 | 6.25 + 0.25*5 = 7.5 | 6 − 7.5 = −1.5 |
| 4 | 7 | 18 | 6.25 + 0.25*7 = 8 | 18 − 8 = 10 |
| 5 | 9 | 10 | 6.25 + 0.25*9 = 8.5 | 10 − 8.5 = 1.5 |
Input:
Output:
Residuals indicate how far off the predicted values are from actual observations, helping to evaluate the quality of a regression model. Online residual calculators improve precision and accuracy in regression analysis.
From NZmaths.co.nz: Residual, Linear Regression
From Originlab.com: Residual Plot Analysis
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