A Comparative Study of the Impact of Dummy Variables on Regression Coefficients and Canonical Correlation Indices: An Empirical Perspective
Bibliographic record
Abstract
In this paper, the impact of dummy variables on regression coefficients and canonical correlation indices from an empirical perspective is investigated. To do this, a regression analysis of Crude Oil Prices on US dollars - Naira Exchange Rates is performed, and the extent of the significance of the relationship is noted. Secondly, dummy variables (coded with respect to various economic regimes of interest) is introduced into the regression of the two variables and the impact of such introduction is also noted. And also, a canonical correlation analysis (CCA) of Inflation rate, the dummy variables and Crude Oil Prices and the dummy variables is conducted. Finally, we compare the significant role of the introduction of the dummy variables on the coefficients of the regression and the canonical correlation indices. The results showed that the introduction of dummy variables impact more on the canonical correlation indices than it does on the regression coefficients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".