The residual scatter plot is the vertical distance information set factor and a regression line. The residual plot examines the linear regression approach. It rapid appraises the degree to which the found values deviated from the projected values. Residual plot creator figures usually coast from the forecast statement as in the Sum of square errors (SSE). The residual scatterplot clarifies the deviation of the distinction among the discovered values and predicted values.
There are one of a kind kinds of residual plots:
To prepare a residual line plot, you need the predicted values as well as the residuals.
After calculating the regression equation, we can compute the predicted values by substituting the independent variable into the regression equation. \( \hat{Y} = 5 + 0.30X \)
The predicted and residual values are displayed in the table below:
| Obs. | X | Y | Predicted Values | Residuals value=(Y-P.V) |
| 1 | 2 | 4 | 5 + 0.30 × 2 = 5.60 | 4 - 5.60 = -1.60 |
| 2 | 10 | 6 | 5 + 0.30 × 10 = 8.00 | 6 - 8.00 = -2.00 |
| 3 | 4 | 8 | 5 + 0.30 × 4 = 6.20 | 8 - 6.20 = 1.80 |
| 4 | 8 | 16 | 5 + 0.30 × 8 = 7.90 | 16 - 7.90 = 8.10 |
| 5 | 12 | 18 | 5 + 0.30 × 12 = 8.60 | 18 - 8.60 = 9.40 |
The residual plot calculator attracts the graph among the found values and actual values through the following method:
Input:
Output:
Heteroskedasticity refers to conditions in which the variance of the residuals indicates too much difference between the real and determined values. The residual graph calculator makes certain the residual factors are displaying Heteroskedasticity or not.
If the residual points inside the residual scatter plot appear inside the curved sample. It way the regression version you have got particular is not accurate.
