Interpret Logistic Regression Coefficients for Data-Driven Decision Making

Act as a statistician and help me thoroughly interpret the results of my logistic regression model so I can derive actionable insights. **Model Details:** - Dependent variable: [TARGET_VARIABLE e.g., purchase_made (1=yes, 0=no)] - Independent variables and their coefficients: [PASTE_COEFFICIENT_TABLE with variable names, coefficients, std errors, p-values, and confidence intervals] - Sample size: [N] - Preprocessing applied: [LIST_ANY_SCALING_ENCODING e.g., StandardScaler on continuous vars, one-hot encoding on categoricals] - Model fit statistics: [PSEUDO_R_SQUARED, LOG-LIKELIHOOD, AIC if available] **Please provide a complete interpretation covering:** 1. **Odds Ratio Conversion:** Convert each coefficient to an odds ratio and interpret it in plain English. For each variable, complete the sentence: "A one-unit increase in [variable] is associated with a [X]% increase/decrease in the odds of [target event], holding all other variables constant." 2. **Statistical Significance Assessment:** Identify which predictors are statistically significant at the 0.05 and 0.01 levels. For non-significant predictors, discuss whether to retain or remove them and why. 3. **Effect Size Ranking:** Rank the predictors by practical importance. Explain why raw coefficient magnitude can be misleading and how standardization affects interpretation. 4. **Interaction & Multicollinearity Checks:** Suggest which variable pairs should be checked for interactions or multicollinearity, and explain warning signs in the current output. 5. **Assumption Validation Checklist:** List the key assumptions of logistic regression (linearity of log-odds, independence, no multicollinearity, sufficient sample size per predictor) and flag any concerns based on my setup. 6. **Business Narrative:** Write a paragraph summarizing the key drivers of [TARGET_VARIABLE] that I can include in a report for decision-makers. 7. **Limitations Disclaimer:** Draft a brief limitations section acknowledging what this model can and cannot claim (correlation vs. causation, generalizability).