Information for Authors: Is the Advice Regarding the Reporting of Residuals in Regression Analysis Incomplete? Should Cook’s Distance Be Included?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
If regression analysis is used for statistical evaluation of the data, authors must supply … standard deviations of residuals (Sy|x, often called standard errors of estimates)… Residuals plots [e.g., Bland-Altman] are often useful . —Extract from “Information for Authors” (2006) The Clinical Chemistry “Information for Authors” recommends that, when regression analysis is used, SDs of residuals must be supplied. (They are not always provided.) As Cook and Weisberg note (1), this conceptual approach dates back to the early 1960s, but by the late 1970s, attention was increasingly directed to assessing the influence of individual observations on the results of regression analysis. The concept of influence (or leverage) can be illustrated by 2 simple examples. In Fig. 1A⇓ , the regression line is shown for 4 in-line cases. When case 5 is added, the new regression line is slightly leveraged toward it (Fig. 1C⇓ ), but note that the case 5 residual is large (Fig. 1E⇓ ) and the regression lines are nearly parallel. However, when case 5 (Fig. 1B⇓ ) is added, the new regression line is much more influenced by its presence (Fig. 1D⇓ ). This case forces the regression line close to it, and its residual is correspondingly small (Fig. 1F⇓ ). What are the differences between these 2 cases? When an outlier is close to the mean value of x (as in case 5 in Fig. 1A⇓ ), its influence is small (Fig. 1C⇓ ), whereas when the outlier is a long way from the mean value of …
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it