{"id":"W2551508380","doi":"10.1002/0470011815.b2a09033","title":"Multiple Linear Regression","year":2005,"lang":"en","type":"other","venue":"Encyclopedia of Biostatistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Linear regression; Statistics; Mathematics; Regression analysis; Value (mathematics); Variable (mathematics); Variables; Regression diagnostic; Regression; Proper linear model; Linear model; Linear predictor function; Statistical inference; Inference; Econometrics; Connection (principal bundle); Contrast (vision); Ordinary least squares; 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":[0.01226388,0.002459263,0.002183521,0.002813628,0.0009740485,0.00532574,0.003479922,0.002148045,0.03939754],"category_scores_gemma":[0.066764,0.0007912781,0.002225661,0.007402681,0.001311984,0.003680073,0.003045198,0.003340469,0.02580378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001577121,"about_ca_system_score_gemma":0.003049009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003590274,"about_ca_topic_score_gemma":0.002767679,"domain_scores_codex":[0.9818249,0.00967386,0.001017411,0.003348535,0.003723393,0.0004119054],"domain_scores_gemma":[0.964915,0.02195993,0.002965611,0.004595951,0.005132153,0.0004314084],"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":[0.0002934413,0.0002487568,0.01525007,0.003130318,0.001237,0.0004332734,0.0006914876,0.0289518,0.002636802,0.1155466,0.1240623,0.7075182],"study_design_scores_gemma":[0.0001369844,0.0006513208,0.01688764,0.002130277,0.0007508484,0.001073182,0.0008082676,0.1569895,0.005996351,0.2165786,0.5976695,0.0003276137],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006630603,0.008292886,0.9333144,0.003291078,0.001766809,0.0006423895,0.006610383,0.004748128,0.03470341],"genre_scores_gemma":[0.1874304,0.0125477,0.7193312,0.001901918,0.001506611,0.002142421,0.01016256,0.002728943,0.06224827],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03939754,"threshold_uncertainty_score":0.1317979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05268072408409614,"score_gpt":0.3951688789303794,"score_spread":0.3424881548462833,"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."}}