{"id":"W2474582416","doi":"10.6000/1929-6029.2016.05.02.4","title":"Confidence Intervals for the Population Correlation Coefficient","year":2016,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Confidence interval; Statistics; Correlation coefficient; Bivariate analysis; Mathematics; Population; CDF-based nonparametric confidence interval; Correlation; Robust confidence intervals; Monte Carlo method; Fisher transformation; Pearson product-moment correlation coefficient; Coverage probability; Range (aeronautics); Standard deviation; Demography; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.008998572,0.00007443418,0.0001901748,0.0002115565,0.00006501451,0.0000374722,0.0006330528,0.00007620445,0.0003383271],"category_scores_gemma":[0.1079521,0.0000384894,0.00004983276,0.0001104966,0.0002603705,0.0001018768,0.00009378811,0.0004100085,0.000004694024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002540935,"about_ca_system_score_gemma":0.0001855748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002606531,"about_ca_topic_score_gemma":0.00005066425,"domain_scores_codex":[0.9959022,0.0003657025,0.000810063,0.0001298476,0.002557584,0.0002345359],"domain_scores_gemma":[0.9512726,0.04620924,0.0002565107,0.0001141081,0.002015502,0.0001320346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001953571,0.00007326675,0.0002119284,0.00001173011,0.00002622024,0.00004500715,0.00008985419,0.00005905304,0.00004623684,0.7316758,0.002507002,0.2650585],"study_design_scores_gemma":[0.0009553605,0.0001626665,0.001440266,0.0004904054,0.000008124057,0.00003895731,0.00007085765,0.0326557,0.0000613654,0.962077,0.001987932,0.00005142732],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00108386,0.00006329606,0.9919441,0.005555458,0.0009209302,0.0002166685,0.0001098794,0.000003469637,0.0001023552],"genre_scores_gemma":[0.7208536,0.0003945181,0.2779574,0.0001154055,0.0003708796,0.00002258861,0.000003501513,0.00001516519,0.0002670237],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7197697,"threshold_uncertainty_score":0.8995619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2731869428025054,"score_gpt":0.5906416889266166,"score_spread":0.3174547461241112,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}