{"id":"W2950359167","doi":"10.48550/arxiv.1805.05756","title":"Visualizing Tests for Equality of Covariance Matrices","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Covariance; Multivariate analysis of variance; Covariance matrix; Multivariate statistics; Variety (cybernetics); Focus (optics); Analysis of covariance; Canonical correlation; Computer science; Simple (philosophy); Plot (graphics); Mathematics; Statistics; Econometrics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01648114,0.001486201,0.001523911,0.005811793,0.0008511186,0.003830347,0.001323824,0.001334574,0.01589733],"category_scores_gemma":[0.1458264,0.0004798586,0.00126549,0.00382771,0.002414617,0.004206598,0.003265471,0.002550608,0.002402239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000686228,"about_ca_system_score_gemma":0.001182282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001043893,"about_ca_topic_score_gemma":0.0006562599,"domain_scores_codex":[0.987053,0.008358097,0.0005978684,0.001409467,0.002299682,0.0002819583],"domain_scores_gemma":[0.8850776,0.09979591,0.004789646,0.00546947,0.004109325,0.0007580731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007534552,0.00020321,0.0250693,0.00160666,0.0007219269,0.0005650286,0.004146261,0.03825229,0.009716708,0.4420561,0.03111646,0.4457926],"study_design_scores_gemma":[0.0001585305,0.0003429635,0.0178241,0.0005085007,0.0001372064,0.0005340585,0.001377181,0.1044853,0.0087976,0.8268796,0.03878415,0.0001709448],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03701786,0.001351616,0.9421022,0.001431717,0.0002850439,0.0001445662,0.00249108,0.005763921,0.009412009],"genre_scores_gemma":[0.5316094,0.001064328,0.4568478,0.0005268934,0.0002727854,0.0007789177,0.003794982,0.002777419,0.002327431],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01648114,"threshold_uncertainty_score":0.08716166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4518116772893424,"score_gpt":0.394434521533,"score_spread":0.05737715575634239,"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."}}