Using Residual Plots to Distinguish Cases of Predictor Omission in Linear Models
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
Residual plots are commonly used to diagnose possible model misspecification, including predictor omission. In this paper, we present a systematic workflow for using residual plots and partial residual plots to detect and distinguish several types of model misspecification in linear models. Our workflow uses a set of four Yes/No questions and is accessible to statisticians and practitioners of all experience levels. Types of model misspecification considered by our workflow include four cases of predictor omission and two types of nonconstant variance. In particular, these cases of predictor omission are defined by the correlation and interaction status between the omitted predictor and the predictor included in the fitted model. Distinguishing cases of predictor omission is important because the impact of predictor omission can vary among cases. The interpretation of the parameter estimates in the statistical model can change depending on the approach.
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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 | 0.001 | 0.005 |
| 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 it