| Multiple Linear Regression - Estimated Regression Equation |
| V1[t] = + 143.317 + 1.09808V2[t] -1.53468V3[t] -0.530988t + e[t] |
| Multiple Linear Regression - Ordinary Least Squares | |||||
| Variable | Parameter | S.D. | T-STAT H0: parameter = 0 | 2-tail p-value | 1-tail p-value |
| (Intercept) | 143.317 | 34.333 | 4.174 | 0.00109056 | 0.000545281 |
| V2 | 1.09808 | 0.278909 | 3.937 | 0.00170272 | 0.00085136 |
| V3 | -1.53468 | 0.258513 | -5.937 | 4.93126e-05 | 2.46563e-05 |
| t | -0.530988 | 0.85369 | -0.622 | 0.544702 | 0.272351 |
| Multiple Linear Regression - Regression Statistics | |
| Multiple R | 0.976058 |
| R-squared | 0.95269 |
| Adjusted R-squared | 0.941772 |
| F-TEST (value) | 87.2604 |
| F-TEST (DF numerator) | 3 |
| F-TEST (DF denominator) | 13 |
| p-value | 7.25901e-09 |
| Multiple Linear Regression - Residual Statistics | |
| Residual Standard Deviation | 5.68933 |
| Sum Squared Residuals | 420.79 |
| Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
| Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
| 1 | 99.2 | 93.9667 | 5.23333 |
| 2 | 99 | 96.3542 | 2.6458 |
| 3 | 100 | 98.063 | 1.93698 |
| 4 | 111.6 | 117.339 | -5.73863 |
| 5 | 122.2 | 123.1 | -0.899841 |
| 6 | 117.6 | 122.709 | -5.10901 |
| 7 | 121.1 | 122.224 | -1.12431 |
| 8 | 136 | 135.311 | 0.689039 |
| 9 | 154.2 | 150.976 | 3.22377 |
| 10 | 153.6 | 153.266 | 0.334051 |
| 11 | 158.5 | 155.074 | 3.42591 |
| 12 | 140.6 | 145.784 | -5.18361 |
| 13 | 136.2 | 144.663 | -8.46254 |
| 14 | 168 | 162.331 | 5.66924 |
| 15 | 154.3 | 156.174 | -1.87428 |
| 16 | 149 | 155.072 | -6.07177 |
| 17 | 165.5 | 154.194 | 11.3059 |
