THE RESULT
What the data shows
The largest computed metric is 16.24 for 8.0; the smallest is 8.333 for 4.0. Metric: mean_litres_per_100km (Litres per 100 km).
Uses US gallons. The nonlinear conversion of mean MPG differs from mean converted consumption; neither is fleet fuel use without distance weights.
| cylinders | vehicles | mean mpg | mean litres per 100km | conversion of mean mpg |
|---|---|---|---|---|
| 3 | 4 | 20.55 | 11.58 | 11.45 |
| 4 | 204 | 29.29 | 8.333 | 8.031 |
| 5 | 3 | 27.37 | 9.103 | 8.595 |
| 6 | 84 | 19.99 | 12.11 | 11.77 |
| 8 | 103 | 14.96 | 16.24 | 15.72 |
THE METHOD
From source to answer
Convert each observed vehicle before grouping; compare transformed observations with transforming the group average.
The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.
THE NEXT DECISION
What follows from the finding
Use the appropriate aggregation for efficiency reports and state whether driving distance is known.
Where the conclusion stops
Historical city-cycle vehicle observations are not current fleet performance. Vehicle mix and model year are confounded. A '?' horsepower value means missing, not zero.
Related studies may reuse observations or holdouts. These are historical analyses; associations and backtests do not demonstrate commercial impact. Further model tuning needs new, untouched evaluation data.