Interpret Logistic Regression Coefficients for Business-Ready Insights

You are a statistical modeling expert who excels at translating technical results into business-friendly insights. Help me interpret my logistic regression model. **Model Details:** - Dependent variable: [TARGET_VARIABLE — e.g., customer churn (1=churned, 0=retained)] - Independent variables and their coefficients: [PASTE_COEFFICIENTS — include variable name, coefficient, standard error, p-value, and confidence interval for each] - Preprocessing applied: [DESCRIBE — e.g., standardized, one-hot encoded, log-transformed] - Sample size: [N] - Model fit statistics: [PASTE — e.g., AIC, BIC, pseudo R², log-likelihood, Hosmer-Lemeshow test] **Please deliver the following:** 1. **Odds Ratio Interpretation**: Convert each coefficient to an odds ratio and write a plain-English interpretation for each. For continuous variables, interpret a one-unit (and one-standard-deviation) increase. For categorical variables, interpret relative to the reference category. Use the format: "Holding all else constant, a [unit] increase in [variable] is associated with a [X]% increase/decrease in the odds of [outcome]." 2. **Statistical Significance Assessment**: Categorize variables into highly significant (p<0.01), significant (p<0.05), marginally significant (p<0.10), and non-significant. Discuss whether non-significant variables should be retained or removed. 3. **Effect Size Ranking**: Rank variables by practical importance (not just statistical significance). Explain why standardized coefficients or odds ratios are more appropriate for this comparison. 4. **Multicollinearity & Assumption Checks**: Based on the coefficients and standard errors, flag any signs of multicollinearity. List the key assumptions of logistic regression and suggest diagnostics I should run. 5. **Business Narrative**: Write a 3-4 paragraph executive summary that a non-technical stakeholder could understand, highlighting the top 3 drivers of [TARGET_VARIABLE] and their practical implications. 6. **Limitations & Caveats**: Note important limitations of interpreting these coefficients causally, and any domain-specific concerns.