| Multiple Linear Regression - Estimated Regression Equation |
| y[t] = + 118.4225 -11.0545x[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) | 118.4225 | 0.653717 | 181.1526 | 0 | 0 |
| x | -11.0545 | 1.054087 | -10.4873 | 0 | 0 |
| Multiple Linear Regression - Regression Statistics | |
| Multiple R | 0.953455876723921 |
| R-squared | 0.909078108859382 |
| Adjusted R-squared | 0.900812482392053 |
| F-TEST (value) | 109.982965290367 |
| F-TEST (DF numerator) | 1 |
| F-TEST (DF denominator) | 11 |
| p-value | 4.58642249956398e-07 |
| Multiple Linear Regression - Residual Statistics | |
| Residual Standard Deviation | 1.84899063373408 |
| Sum Squared Residuals | 37.6064299999999 |
| Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
| Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
| 1 | 119.73 | 118.4225 | 1.30749999999996 |
| 2 | 119.67 | 118.4225 | 1.24750000000000 |
| 3 | 119.67 | 118.4225 | 1.24750000000001 |
| 4 | 119.5 | 118.4225 | 1.07750000000000 |
| 5 | 119.39 | 118.4225 | 0.967500000000005 |
| 6 | 119.28 | 118.4225 | 0.857500000000006 |
| 7 | 117 | 118.4225 | -1.42250000000000 |
| 8 | 113.14 | 118.4225 | -5.2825 |
| 9 | 107.46 | 107.368 | 0.0919999999999958 |
| 10 | 107.41 | 107.368 | 0.0419999999999986 |
| 11 | 107.39 | 107.368 | 0.0220000000000026 |
| 12 | 107.31 | 107.368 | -0.0579999999999957 |
| 13 | 107.27 | 107.368 | -0.098000000000002 |
