Analytical Problem Solving Based on Causal, Correlational and Deductive Models

Many approaches for solving problems in business and industry are based on analytics statistical modeling. Analytical problem is driven by the modeling of relationships between dependent (Y) independent (X) variables, we discuss three frameworks such relationships: cause-and-effect modeling, popular applied statistics beyond, correlational predictive machine learning, deductive (first-principles) operations research. We aim to explain differences these types models, flesh out implications study design, discovering potential X/Y relationships, solution patterns that each type could support. use our account clarify descriptive-diagnostic-predictive-prescriptive framework, but extend it offer a more complete model process analytical solving, reflecting essential causal, correlational, models.