Overview
Regression analysis is the most commonly misinterpreted statistical method in business analytics. R² is reported as if it measures predictive accuracy (it doesn't — it measures explained variance). Coefficients are interpreted as causal effects (they aren't — they are associations conditional on the other variables in the model). Statistical significance is confused with practical importance. And the assumption checks that determine whether the results are valid are skipped entirely.
A defensible regression analysis requires four things beyond running the model: assumption diagnostics that validate the results, coefficient interpretation that correctly states what the model says, effect size assessment that distinguishes statistical from practical significance, and an honest statement of what the model cannot tell you — particularly about causation.
The Regression Analysis & Interpretation Prompt generates a complete regression analysis framework: assumption diagnostics, coefficient interpretation in business terms, model fit assessment, and a communication template that explains the results to non-technical stakeholders without overstating what the model proves.
What you get: - Assumption diagnostic protocol with remediation - Coefficient interpretation in business units - Model fit assessment beyond R² - Causation vs. correlation clarification - Non-technical results communication template
Built for: analysts and data scientists who need to run, validate, and communicate regression results to business audiences who will make decisions based on them.