Statistical investigation: vehicle efficiency, model revision and changed targets: changed case
Address this changed case in a fresh argument. Explain which earlier conclusions survive and which require new evidence. This is public practice with available guidance; it is not an untouched examination. Your responses and guidance exposure are stored separately from the main project.
Data investigation: fit, freeze and challenge
This optional calculator supports the analysis. Your report must explain the question, design, evidence and revision. Fits and comparisons here are temporary. Written milestone revisions are saved separately by the project page.
A real dataset with a reproducible study
Source: Quinlan, R. (1993), Auto MPG, UCI Machine Learning Repository, CC BY 4.0. This adaptation selects model-year codes 70–74 from the 398-row processed source, retains five source fields and adds one-based source-row IDs. That source already excluded eight records with missing MPG. Missing horsepower remains missing. Retrieved 26 October 2026; attribution, changes and source hash are included below.
Development: 1970–72. Public validation: 1973. Separate transfer practice: 1974. These historical cohorts are not a random sample, a secure examination or evidence about current vehicles. Downloading the pack exposes every cohort; the buttons cannot make it unseen. Record earlier access, solutions and assistance in your report.
This session has displayed 1973 evaluation: not through this calculator; 1974 evaluation: not through this calculator. This does not track downloads or earlier sessions. Repeated evaluation of public rows is development practice, not independent new evidence. Do not use 1974 results retrospectively to choose a model supposedly selected on 1973.
0 of 1 transfer tasks have recorded responses. This counts submissions, not quality, independent review or project mastery.
Changed target: fuel use for equal-distance travel
Change the question for year-74 records: estimate gallons per 100 miles for equal-distance vehicle use, c=100/MPG. Derive why averaging this response is not the same as taking 100 divided by average MPG; use MPG 20 and 40 as an exact check and state the population-integrability conditions. Before reading year-74 outcomes, fit and freeze the development-only consumption baseline, weight-only model and weight-plus-horsepower model. Declare consumption MSE as the selection criterion, use the already exposed year-73 cohort to choose among them, and record that development decision before any year-74 comparison. The mandatory models use the same w and h predictors as the original study. Compare their consumption predictions with reciprocals of the original frozen MPG candidates on the same eligible year-74 rows. Report invalid nonpositive MPG predictions or nonphysical consumption predictions explicitly; never drop failed predictions to improve a loss ranking. Retain original MPG results, new response units, SSE/MSE/MAE and the model-selection/exposure record. Explain which question each model answers and whether a reciprocal mean or transformed interval supplies the desired conditional consumption statement.
Structure your argument and preserve revisions
These optional planning fields save locally. Recording your response saves an immutable copy of the argument and these fields. Older records may contain only first/latest responses; missing intermediate revisions are not reconstructed.
Each field allows 2,000 characters. Keep a specific claim, state where it applies, cite the facts you use, and explain what still needs checking. Blank fields are allowed and are not evidence of mastery.
Review, defend and revise
Ask a reviewer to examine the complete reasoning, reproduce selected results and pose an unfamiliar case. Record their feedback against the specific revision discussed. Reviewer labels are entered locally and do not verify identity or judgment. A complete set of text submissions does not certify an independently defended investigation.
Targeted lesson returns
Changed target: fuel use for equal-distance travel
