{"id":"W4379390207","doi":"10.32920/23296004.v1","title":"Experiment With Multiple Regression Models for Sales Forecast and Location Analysis","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"","keywords":"Projection (relational algebra); Regression analysis; Computer science; Polygon (computer graphics); Sales management; Sales forecasting; Range (aeronautics); Econometrics; Operations research; Business; Marketing; Economics; Engineering; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.021924,0.001268967,0.001503832,0.001080899,0.0006739512,0.002042134,0.002288545,0.001648014,0.008293563],"category_scores_gemma":[0.05184024,0.0006838982,0.002358225,0.002226948,0.0006034249,0.004002684,0.001327979,0.003449318,0.001927161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001387959,"about_ca_system_score_gemma":0.001168342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01935695,"about_ca_topic_score_gemma":0.01089231,"domain_scores_codex":[0.9872744,0.01012518,0.0003286219,0.001361866,0.0005863797,0.0003235632],"domain_scores_gemma":[0.9062898,0.08518552,0.001927926,0.00334336,0.002732393,0.0005210144],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00209983,0.001696932,0.03431866,0.0002505681,0.001102128,0.0002148029,0.0005820135,0.7975803,0.00156869,0.03119498,0.005699469,0.1236918],"study_design_scores_gemma":[0.00005922376,0.0001477723,0.001288273,0.00000793684,0.00003337286,0.00001061105,0.00005242577,0.9938055,0.0004330075,0.003452035,0.0006888538,0.00002092272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3708656,0.0007440611,0.6136894,0.002180523,0.0003987474,0.0003849208,0.001628071,0.002776673,0.007332004],"genre_scores_gemma":[0.6997386,0.0003046602,0.2931442,0.0001883886,0.000160347,0.0004862779,0.001564328,0.0003752708,0.004037973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.021924,"threshold_uncertainty_score":0.1159466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09001631662274426,"score_gpt":0.2589284146984545,"score_spread":0.1689120980757102,"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."}}