Distribution diagnostics and calibration
Inspect distribution discrepancies, derive valid null references, refit estimated models and defend categorical model revisions.
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Readiness and preparation
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Audit an unfamiliar distribution-fit investigation
The mixed assessment has separate response history and review criteria; it does not change lesson participation records.
Lesson sequence
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Empirical CDFs and QQ evidence
Lesson details and objectives
Compute both sides of tied empirical jumps and explain QQ plotting positions.
- Compute both sides of tied empirical jumps and explain QQ plotting positions.
- Distinguish descriptive discrepancy from calibrated evidence.
6 practice tasks.
Specified continuous-null calibration
Lesson details and objectives
Prove the continuous probability-integral transform.
- Prove the continuous probability-integral transform.
- Justify the exchangeable Monte Carlo rank test and diagnose invalid reference procedures.
6 practice tasks.
Fitted nulls and parametric bootstrap
Lesson details and objectives
Derive the exponential rate fit and scale-invariant discrepancy.
- Derive the exponential rate fit and scale-invariant discrepancy.
- Refit every reference sample and distinguish a special pivot from a general bootstrap approximation.
6 practice tasks.
Categorical fit and model revision
Lesson details and objectives
Compute specified and fitted Pearson discrepancies.
- Compute specified and fitted Pearson discrepancies.
- Explain projection degrees of freedom, boundary failures and independent model revision.
6 practice tasks.