= Solution
The transformed values are <Cox–Snell residuals>. Under a correct common <survival function> and a good fitted <cumulative hazard function>, the complete transformed failure times should have unit <exponential distribution>. Transform the observed times and retain their original <censoring> indicators, then estimate residual survival with a <Kaplan–Meier estimator>. Its target is
$$
\boxed{S_u(u)=e^{-u},\qquad u\geq0,}
$$
so a plot of its logarithm against $u$ should lie approximately on a line of slope $-1$. Censored residuals must not be treated as fully observed failure times.
There is a useful caution for this particular common nonparametric fit. The <shared-fit Cox–Snell residuals reproduce the fitted survival curve> property means that the pooled residual plot is largely built into the transformation. With event ordering and compatible tie processing preserved, at a transformed failure time $u_j=\widehat H(t_j)$ its residual <Kaplan–Meier estimator> equals $\widehat S(t_j)=e^{-u_j}$. Finite steps, coalesced <censoring> times and estimation uncertainty still matter, and an infinite final residual arises if the fitted survivor becomes zero. Separate group plots are more informative about a common-distribution assumption than this pooled fit alone.
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