Bibliographic record
Abstract
Statistical analysis is one of the key aspects of engineering studies by researchers and transportation agencies. It is common to use regression analysis to develop a relationship between a response and one or more regressor variables. However, in many instances correlations between different variables are suggested and used for the prediction of an event without proper evaluation or interpretation of the developed models. This procedure may lead to gross errors, particularly when many senior engineers are retiring and leaving behind successors with limited skills. The issue is further complicated because engineers receive limited exposure to statistics during their undergraduate program and consequently must learn on the job; this situation can result in some key aspects to statistical analysis being missed. For example, the coefficient of determination, which can be meaningless in many circumstances, is used by many people to evaluate the adequacy of a model. Points of intersection of two or more trend lines are sometimes taken as the optimum value of various criteria without considering practical or engineering aspects. This paper focuses on some important aspects of statistical analysis and shows through various pavement engineering and management models how a model and resulting conclusion can be misleading. Proper evaluation of the causal relationship among variables and interpretation of the model are important to support a model's usefulness as well as to draw meaningful conclusions from it. A simple and concise approach is presented to aid researchers and practitioners with regression modeling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".