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Let x be the explanatory variable weekly training distance (km) and y the response variable fitness test total (points). Apply the square to the response variable only. Use the full fitted coefficients below. Report signed residuals to 1 decimal places; retain unrounded values until that final step

\[X=x,\quad Y=y^{2}\]

Use weekly training distance (km) to model fitness test total (points) for the displayed cases. Apply a square transformation to the response variable only

Caseweekly training distance (km)fitness test total (points)
Case 112381
Case 216399
Case 320309
Case 424315
Case 528237
Case 632267
Case 736201
Case 840207

The fitted equation is written on the transformed coordinates. Residuals are calculated on the fitted line's own response scale: signed residual on the fitness test total² (points²) scale

A signed residual is observed transformed response minus predicted transformed response at the same transformed explanatory coordinate

\[\widehat{Y}=199053+(-\frac{8487}{2})X\]

The residual plot is shown below. A horizontal zero line is included

Residual plot with 8 points. The horizontal axis is weekly training distance (km) and the vertical axis is signed residual on the fitness test total² (points²) scale. The horizontal coordinate runs from 12.000 to 40.000 and the vertical coordinate runs from -24,066.0 to 28,044.0. The plotted coordinate pairs are listed in displayed data order: (12.000, -2,970.0), (16.000, 28,044.0), (20.000, -18,702.0), (24.000, 2,016.0), (28.000, -24,066.0), (32.000, 8,028.0), (36.000, -5,886.0), (40.000, 13,536.0). A horizontal reference line is drawn at zero.10203040-20,000020,000weekly training distance(km)signed residual on thefitness test total²(points²) scale
(a)1 mark

Classify the structure in the displayed residual plot

(b)1 mark

Does the residual plot support using a linear model for the transformed data?