1{2 "verdict": "accept",3 "issues": [4 {5 "id": "R1",6 "severity": "minor",7 "location": "out/claims.json C3",8 "issue": "C3 ('HC1 still falls short of 95%') is marked 'supported', but its own caveat says the shortfall was not formally tested. The evidence is strong ((0.95-0.902)/0.0066 ≈ 7.2 MCSE, as the report says), but the status and the caveat do not quite match.",9 "suggested_fix": "Put the 7-MCSE comparison into the claim or caveat (e.g. 0.902 < 0.95 - 3*0.00487) so that 'supported' rests on a stated test."10 },11 {12 "id": "R2",13 "severity": "minor",14 "location": "out/report.md Data/Results; claims.json C2",15 "issue": "The mechanism in C2 depends on corr(sigma, |x - xbar|) = 0.84. I reproduced this value (0.837), but it is not stored in results.json and the code does not compute it. Also, mean se_conv < SD(beta_hat) shows that the SE is too small, but saying that leverage-weighted heteroskedasticity is the cause is an interpretation.",16 "suggested_fix": "Add the correlation to results.json as a supplementary key and word C2 as an explanation that is consistent with the design."17 }18 ],19 "summary": "Re-running analysis.py in a scratch copy reproduced results.json exactly. An independent statsmodels loop over all 2000 replications (same seed and draws) gave coverage_conventional = 0.844, coverage_hc1 = 0.902 and mean_se_conventional = 0.19026, all matching. The analytic sd_beta_exact = 0.26798 also matches. All required keys are present with correct types. The code uses only data/design.csv, keeps x and sigma fixed, uses R = 2000 with the seed recorded, uses n-2 divisors and the t(28) critical value, and computes HC1 as HC0 times n/(n-2). The undercoverage flag follows the 3-MCSE rule, and the conclusion holds under either reading of the binomial SE. The report gives MCSEs for both coverages and states the scope limits. Two minor wording issues only."20}21