Weighting and Mileage Coding in Grouped Vehicle Emission Regression

Weighting rules and mileage coding can change the apparent fit, uncertainty, and predictive performance of regression models built from grouped emission summaries. We examine these choices in a computational case study of 18 supplied age–mileage cells for carbon monoxide, non-methane hydrocarbons, and nitrogen oxides. Age-only, ordinal-mileage, and categorical-mileage specifications are compared using four weighting rules, cell deletion, robust regression, and cross-validation. Categorical mileage represents the lower middle-class means that ordinal coding cannot capture and improves prediction within the supplied grid, including when an entire age level is withheld. However, its advantage is conditional on the mean pattern and can reverse under a correctly specified ordinal model or contamination at the reference cell. Additional simulations with estimated cell dispersions show that interval coverage depends on covariance choice, contamination location, and model specification; small residual-based standard errors do not establish population precision. For practice, weights should reflect a stated estimation target, mileage coding should be checked against the observed shape, and fit should be assessed separately from prediction. The supplied counts sum to 1000, but their measurement lineage and disagreement with assignment frequencies remain unresolved. The findings therefore concern the computational behavior of supplied summaries and constructed scenarios, rather than causal effects or a verified vehicle population.