{"id":"W4366392214","doi":"10.5539/ibr.v16n5p12","title":"Using Statistics for Market Analysis Forecasting","year":2023,"lang":"en","type":"article","venue":"International Business Research","topic":"Innovation Diffusion and Forecasting","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quality (philosophy); Regression analysis; Computer science; Identification (biology); Market analysis; Reliability (semiconductor); Demand forecasting; Operations research; Econometrics; Economics; Marketing; Business; Engineering; Machine learning","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":[0.01398506,0.001731268,0.001795121,0.01104847,0.001004643,0.005930834,0.001543177,0.002147333,0.008271554],"category_scores_gemma":[0.08041573,0.0008206735,0.001627148,0.01160399,0.002682809,0.007706903,0.002380814,0.005668059,0.005078341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002002939,"about_ca_system_score_gemma":0.002664583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002330415,"about_ca_topic_score_gemma":0.00153874,"domain_scores_codex":[0.9849058,0.007878756,0.001208721,0.001274664,0.004429513,0.0003025661],"domain_scores_gemma":[0.935157,0.05028435,0.004089569,0.005317256,0.004555626,0.0005961252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009490143,0.000126045,0.007050858,0.001309111,0.0003270465,0.0004661006,0.0008684916,0.03806997,0.002490116,0.3198045,0.06870311,0.5606896],"study_design_scores_gemma":[0.00004508516,0.0002188904,0.00458816,0.001166946,0.0001282028,0.0007080683,0.0007041404,0.1469983,0.003347465,0.6002308,0.2416561,0.000207761],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00365395,0.01069366,0.9575117,0.005951488,0.001615394,0.0002403905,0.001346294,0.003430039,0.01555692],"genre_scores_gemma":[0.09294058,0.02031999,0.869636,0.002015252,0.0037963,0.0008150419,0.003204623,0.001490355,0.005781929],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01398506,"threshold_uncertainty_score":0.07396102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6056018698309001,"score_gpt":0.5549532909418379,"score_spread":0.05064857888906227,"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."}}