| Multiple Linear Regression - Estimated Regression Equation |
| Werklh[t] = + 3.40227687770263 -4.01055009279333Consvert.[t] + 1.15270246829753AlgECSit[t] + 1.49518917610675Finsitgez[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) | 3.40227687770263 | 1.442945 | 2.3579 | 0.04275 | 0.021375 |
| Consvert. | -4.01055009279333 | 0.596389 | -6.7247 | 8.6e-05 | 4.3e-05 |
| AlgECSit | 1.15270246829753 | 0.352966 | 3.2658 | 0.009748 | 0.004874 |
| Finsitgez | 1.49518917610675 | 0.723066 | 2.0678 | 0.068603 | 0.034301 |
| Multiple Linear Regression - Regression Statistics | |
| Multiple R | 0.962552638277094 |
| R-squared | 0.926507581454195 |
| Adjusted R-squared | 0.902010108605593 |
| F-TEST (value) | 37.8205371296931 |
| F-TEST (DF numerator) | 3 |
| F-TEST (DF denominator) | 9 |
| p-value | 1.9833404832359e-05 |
| Multiple Linear Regression - Residual Statistics | |
| Residual Standard Deviation | 2.80970953312692 |
| Sum Squared Residuals | 71.0502089448987 |
| Multiple Linear Regression - Actuals, Interpolation, and Residuals | |||
| Time or Index | Actuals | Interpolation Forecast | Residuals Prediction Error |
| 1 | 36 | 33.5055504586452 | 2.49444954135482 |
| 2 | 24 | 23.7496969569825 | 0.250303043017528 |
| 3 | 22 | 20.7115015122729 | 1.28849848772708 |
| 4 | 17 | 23.016906448868 | -6.01690644886799 |
| 5 | 8 | 9.53788408687728 | -1.53788408687728 |
| 6 | 12 | 13.9387379799759 | -1.93873797997588 |
| 7 | 5 | 6.65042830250371 | -1.65042830250371 |
| 8 | 6 | 6.26012450219844 | -0.260124502198437 |
| 9 | 5 | 2.66948636958814 | 2.33051363041186 |
| 10 | 8 | 8.48810418641968 | -0.488104186419682 |
| 11 | 15 | 11.97581975119 | 3.02418024880996 |
| 12 | 16 | 14.8814844681819 | 1.11851553181811 |
| 13 | 17 | 15.6142749762964 | 1.38572502370362 |
