{"id":"W2611906781","doi":"","title":"POSTER: The Impact of Group Imbalance on Logistic Regression Analyses with Assessment Data","year":2016,"lang":"en","type":"article","venue":"ITC 2016 Conference","topic":"Advanced Statistical Modeling Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Statistics; Covariate; Skewness; Type I and type II errors; Logistic regression; Mathematics; Sample size determination; Econometrics; Variables; Wald test; Regression analysis; Analysis of covariance; Variable (mathematics); Statistical hypothesis testing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000283622,0.0001919202,0.0002440381,0.00005369421,0.00007274141,0.00006492263,0.002348322,0.00004247233,0.00002628217],"category_scores_gemma":[0.0002199357,0.00007018404,0.00003577784,0.000154789,0.0002383092,0.0004401772,0.0006066037,0.0001238755,0.000013233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006560954,"about_ca_system_score_gemma":0.0002056193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001582749,"about_ca_topic_score_gemma":0.00001310292,"domain_scores_codex":[0.9984694,0.0001283381,0.0002477968,0.0005294096,0.0003646665,0.0002604392],"domain_scores_gemma":[0.9964623,0.0007385316,0.0002398996,0.002286055,0.0001962506,0.00007700228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000381917,0.0005695319,0.01309874,0.00005192386,0.0002118906,0.00009178778,0.0001438734,0.0002813308,0.08575829,0.4725422,0.007906917,0.4189616],"study_design_scores_gemma":[0.002354508,0.00723895,0.2056795,0.005194171,0.0001013012,0.00008095299,0.00002527498,0.3838249,0.01511306,0.3784063,0.0003806016,0.001600487],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002899741,0.00003510119,0.9949203,0.0007714132,0.00003806609,0.0001678189,0.0001142634,0.0001229372,0.0009303924],"genre_scores_gemma":[0.8822815,0.00006591344,0.1174976,0.00004943277,0.00001602239,0.00001152491,0.000006627967,0.000007771885,0.00006362743],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8793818,"threshold_uncertainty_score":0.4363806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2270099035007783,"score_gpt":0.4714656255143577,"score_spread":0.2444557220135795,"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."}}