| Multiple Linear Regression - Estimated Regression Equation |
| Y-nanogram[t] = -51.4986 + 0.712329`X-weight`[t] + 0.984309t + 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) | -51.5 | 35.53 | -1.4490e+00 | 0.1905 | 0.09526 |
| `X-weight` | +0.7123 | 0.2107 | +3.3810e+00 | 0.01175 | 0.005874 |
| t | +0.9843 | 0.562 | +1.7510e+00 | 0.1233 | 0.06166 |
| Multiple Linear Regression - Regression Statistics | |
| Multiple R | 0.9934 |
| R-squared | 0.9868 |
| Adjusted R-squared | 0.983 |
| F-TEST (value) | 261.7 |
| F-TEST (DF numerator) | 2 |
| F-TEST (DF denominator) | 7 |
| p-value | 2.641e-07 |
| Multiple Linear Regression - Residual Statistics | |
| Residual Standard Deviation | 1.139 |
| Sum Squared Residuals | 9.074 |
| Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
| Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
| 1 | 70 | 69.16 | 0.8431 |
| 2 | 72 | 74.42 | -2.415 |
| 3 | 78 | 77.54 | 0.4635 |
| 4 | 81 | 80.66 | 0.3422 |
| 5 | 84 | 83.07 | 0.9333 |
| 6 | 88 | 87.61 | 0.3873 |
| 7 | 90 | 89.31 | 0.6907 |
| 8 | 91 | 91.72 | -0.7183 |
| 9 | 93 | 93.41 | -0.4149 |
| 10 | 95 | 95.11 | -0.1116 |
