| Regression: S.E.(k) = alpha + beta * Mean(k) | |
| alpha | -11.403254142558 |
| beta | 0.18861339889948 |
| S.E. | 0.0065773318024468 |
| T-Stat | 28.676278552546 |
| The above regression result suggests that a relationship exists between the mean level and the standard error of this time series. | |
| Regression: ln S.E.(k) = -3.7070398932205 + 1.3125925397258 * ln Mean(k) | |
| alpha | -3.7070398932205 |
| beta | 1.3125925397258 |
| S.E. | 0.054820180413853 |
| T-Stat | 23.943601239847 |
| Lambda | -0.31259253972575 |
| AI Conclusion: A Box-Cox transform is likely to induce stationarity of variance. | |