Interpret Logistic Regression Coefficients for Stakeholder Communication

You are a senior statistician and data science communicator. Help me interpret the results of a logistic regression model and prepare explanations suitable for both technical and non-technical audiences. **Model Details:** - Dependent variable: [TARGET_VARIABLE] (binary outcome: [POSITIVE_CLASS] vs. [NEGATIVE_CLASS]) - Independent variables and their coefficients: ``` [PASTE_COEFFICIENTS_TABLE_WITH_STANDARD_ERRORS_AND_P_VALUES] ``` - Preprocessing applied: [SCALING_ENCODING_DETAILS] - Model intercept: [INTERCEPT_VALUE] - Overall model metrics: [ACCURACY_AUC_ETC] - Sample size: [N] **Please provide:** 1. **Odds Ratio Interpretation**: Convert each coefficient to an odds ratio and write a plain-English interpretation sentence for each variable. For continuous variables, explain the per-unit change. For categorical variables, explain relative to the reference category. 2. **Statistical Significance Assessment**: Identify which predictors are statistically significant at the [SIGNIFICANCE_LEVEL] level. Discuss any variables that are practically important but not statistically significant, and vice versa. 3. **Effect Size Ranking**: Rank variables by their practical impact on the outcome. If features were scaled differently, account for this when comparing effect sizes. Recommend using standardized coefficients if appropriate. 4. **Multicollinearity Check Guidance**: Based on the coefficients and signs, flag any suspicious patterns that might indicate multicollinearity. Suggest diagnostic steps (VIF, correlation matrix). 5. **Model Assumptions Review**: List the key assumptions of logistic regression and suggest specific diagnostic tests I should run to validate them (linearity of log-odds, independence, no multicollinearity, sufficient sample size per predictor). 6. **Stakeholder-Ready Summary**: Write a 3-4 sentence non-technical summary of the model's key findings that a business executive could understand, focusing on the top 3 most impactful predictors and their practical meaning. 7. **Limitations Disclaimer**: Draft a brief paragraph noting important caveats (correlation vs. causation, potential confounders, generalizability).