{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00506294,0.000124346,0.0002128332,0.0002725862,0.0002462178,0.0001448669,0.0016169,0.00004522898,0.00001151029],"category_scores_gemma":[0.0005293846,0.0000839598,0.0000480609,0.001522656,0.0005692856,0.000354457,0.0005928154,0.0001022543,0.00002282576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007660824,"about_ca_system_score_gemma":0.0002629706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002700783,"about_ca_topic_score_gemma":0.000004190473,"domain_scores_codex":[0.9962448,0.00001064882,0.0005681795,0.0006296971,0.002212683,0.0003339486],"domain_scores_gemma":[0.9977576,0.0001385884,0.0003772941,0.000905176,0.0005903112,0.0002310444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002944458,0.00009171251,0.0001079426,0.000003927154,0.000001652761,7.521549e-7,0.001661083,0.008601936,0.495923,0.07471601,0.002349386,0.4165132],"study_design_scores_gemma":[0.00008881625,0.00003341117,0.00001384761,0.000007690224,0.000001363468,0.000001813381,0.0001728702,0.5306607,0.04006619,0.4286736,0.0002133272,0.00006635236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1704551,0.00001846812,0.7590078,0.0001707327,0.00007232617,0.0003051215,0.000005894291,0.00008521223,0.06987941],"genre_scores_gemma":[0.94846,8.546087e-7,0.05111415,0.0000729251,0.00002403253,0.00004841336,7.514013e-7,0.000008058631,0.0002707571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.778005,"threshold_uncertainty_score":0.3423781,"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."}}