{"id":"W3148371986","doi":"10.1002/9781118445112.stat05766","title":"Multiple Linear Regression","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Linear regression; Statistics; Mathematics; Regression analysis; Regression diagnostic; Value (mathematics); Variables; Variable (mathematics); Regression; Proper linear model; Linear model; Inference; Statistical inference; Linear predictor function; Contrast (vision); Log-linear model; Econometrics; Bayesian multivariate linear regression; Computer science; Artificial intelligence","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":[0.01363837,0.002415919,0.00217938,0.003036262,0.0009866181,0.005215962,0.003516419,0.002059072,0.04017084],"category_scores_gemma":[0.07508315,0.0008337212,0.002452878,0.007672275,0.001377306,0.003673582,0.00308106,0.003448689,0.02842434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001657461,"about_ca_system_score_gemma":0.003329846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003863077,"about_ca_topic_score_gemma":0.002828265,"domain_scores_codex":[0.9796237,0.01120381,0.001125888,0.003743826,0.00386999,0.00043283],"domain_scores_gemma":[0.9583622,0.02658235,0.003489211,0.005321884,0.005791644,0.0004528056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002999456,0.0002529015,0.01567116,0.003198828,0.001254924,0.0004007768,0.0007164815,0.03099562,0.002513892,0.1239836,0.1372003,0.6835116],"study_design_scores_gemma":[0.0001311544,0.0006343807,0.01700831,0.002268113,0.0007136903,0.0009817076,0.0007643377,0.1716954,0.005329448,0.2326915,0.5674438,0.0003381361],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006169539,0.006484255,0.9384195,0.00296539,0.001600551,0.0006187299,0.007720832,0.005008565,0.0310127],"genre_scores_gemma":[0.1792985,0.01014437,0.7363893,0.001773471,0.001508295,0.002190031,0.01135483,0.003342316,0.05399887],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04017084,"threshold_uncertainty_score":0.1343849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1618857117479399,"score_gpt":0.4480577349044283,"score_spread":0.2861720231564884,"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."}}