| Multiple Linear Regression - Estimated Regression Equation |
| d[t] = -0.430332294448803 -0.437119161948204a[t] + 0.658167216343528b[t] -0.185719923382024c[t] + 2.08835414762538V5[t] + 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) | -0.430332294448803 | 1.343833 | -0.3202 | 0.757 | 0.3785 |
| a | -0.437119161948204 | 0.395321 | -1.1057 | 0.300982 | 0.150491 |
| b | 0.658167216343528 | 0.243087 | 2.7075 | 0.02676 | 0.01338 |
| c | -0.185719923382024 | 0.684027 | -0.2715 | 0.792874 | 0.396437 |
| V5 | 2.08835414762538 | 1.090757 | 1.9146 | 0.091879 | 0.045939 |
| Multiple Linear Regression - Regression Statistics | |
| Multiple R | 0.81095169443103 |
| R-squared | 0.657642650700558 |
| Adjusted R-squared | 0.486463976050837 |
| F-TEST (value) | 3.84184917920574 |
| F-TEST (DF numerator) | 4 |
| F-TEST (DF denominator) | 8 |
| p-value | 0.0498762172047272 |
| Multiple Linear Regression - Residual Statistics | |
| Residual Standard Deviation | 2.14063575707533 |
| Sum Squared Residuals | 36.6585715557556 |
| Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
| Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
| 1 | 6 | 6.89011724104149 | -0.890117241041492 |
| 2 | 6 | 4.61227738595227 | 1.38772261404773 |
| 3 | 0 | 4.48847099367258 | -4.48847099367258 |
| 4 | 6 | 4.61772316598591 | 1.38227683401409 |
| 5 | 2 | 1.24083114852314 | 0.75916885147686 |
| 6 | 2 | 1.86743601793519 | 0.132563982064812 |
| 7 | 2 | 2.57754220177117 | -0.577542201771173 |
| 8 | 3 | 1.38833178217941 | 1.61166821782059 |
| 9 | -1 | -0.709976259130856 | -0.290023740869144 |
| 10 | -4 | -2.16840791652858 | -1.83159208347142 |
| 11 | 4 | 1.99666648908547 | 2.00333351091453 |
| 12 | 5 | 4.05843523662181 | 0.941564763378192 |
| 13 | 3 | 3.14055251289099 | -0.140552512890994 |
