{"id":"W1487620006","doi":"10.5539/mas.v9n6p344","title":"Improvement of Regression Forecasting Models","year":2015,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Российский экономический университет имени Г.В. Плеханова","keywords":"Estimator; Regression; Regression analysis; Mathematics; Statistics; Ordinary least squares; Mean squared error; Linear regression; Applied mathematics; Regression diagnostic; Polynomial regression","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003764743,0.001417668,0.001520721,0.001262423,0.0003746181,0.001053714,0.001671039,0.00118633,0.002899479],"category_scores_gemma":[0.01308036,0.0005559757,0.001791117,0.001360775,0.0003690742,0.001644132,0.001222512,0.001910645,0.002433575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004553417,"about_ca_system_score_gemma":0.0007449758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002474388,"about_ca_topic_score_gemma":0.001852782,"domain_scores_codex":[0.997677,0.001023183,0.0001097587,0.0004379763,0.0006523419,0.00009966375],"domain_scores_gemma":[0.9966491,0.002021952,0.0003097509,0.0004681825,0.0005109453,0.00004007135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001616205,0.0001312281,0.003307479,0.0006208561,0.0003849718,0.0001529588,0.0001968124,0.4004845,0.008067974,0.04031266,0.006118385,0.5400605],"study_design_scores_gemma":[0.00001334625,0.0001017367,0.0007972359,0.00005192593,0.00009562479,0.00009398519,0.00001496161,0.9746175,0.002470952,0.01151378,0.01020224,0.00002655708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007190722,0.00148976,0.9879168,0.0002727275,0.0001477223,0.00002992341,0.0001141117,0.0007050414,0.002133284],"genre_scores_gemma":[0.3594063,0.004758383,0.6233156,0.0003593875,0.0006712498,0.0002461809,0.001031466,0.000690551,0.009520818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003764743,"threshold_uncertainty_score":0.01991016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4091251543488018,"score_gpt":0.4092649670648832,"score_spread":0.0001398127160814888,"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."}}