Interpret Logistic Regression Coefficients and Odds Ratios for Clear Reporting

You are a biostatistician and data science educator. Help me interpret my logistic regression results in a way that is both statistically rigorous and accessible to my audience. **Model Context:** - What the model predicts: [BINARY_OUTCOME, e.g., "whether a patient is readmitted within 30 days (1=yes, 0=no)"] - Domain: [HEALTHCARE/FINANCE/MARKETING/OTHER] - Audience for interpretation: [RESEARCHERS/BUSINESS_TEAM/CLINICAL_STAFF/OTHER] **Model Output:** ``` [PASTE_LOGISTIC_REGRESSION_SUMMARY, including coefficients, standard errors, p-values, and confidence intervals] ``` **Variables of Special Interest:** [LIST_KEY_VARIABLES_YOU_WANT_DEEPLY_INTERPRETED] Please provide a comprehensive interpretation covering: 1. **Coefficient Interpretation:** For each predictor, explain the log-odds coefficient in plain language. Then convert to an odds ratio and explain what it means practically. For example: "A one-unit increase in [VARIABLE] multiplies the odds of [OUTCOME] by X." 2. **Continuous vs. Categorical Variables:** Differentiate interpretation for continuous predictors (per-unit change) versus categorical predictors (relative to reference category). Clarify the reference categories used. 3. **Statistical Significance:** Interpret p-values and confidence intervals for the odds ratios. Highlight which predictors are statistically significant and which are not, while cautioning against over-reliance on p-values alone. 4. **Effect Size and Practical Significance:** Beyond statistical significance, assess whether the effect sizes are meaningful in the context of [DOMAIN]. A statistically significant but tiny odds ratio may not matter practically. 5. **Model Fit Assessment:** Interpret any available goodness-of-fit metrics (pseudo R², AUC-ROC, Hosmer-Lemeshow test, log-likelihood) and explain what they reveal about overall model quality. 6. **Interaction Terms (if present):** If my model includes interaction terms, explain how to interpret them and compute conditional odds ratios. 7. **Common Misinterpretations:** List 3 common mistakes people make when interpreting logistic regression (e.g., confusing odds with probability, interpreting coefficients as linear effects on probability) and correct them. 8. **Report-Ready Paragraph:** Write a professional results paragraph suitable for inclusion in a [RESEARCH_PAPER/BUSINESS_REPORT/CLINICAL_SUMMARY] that accurately communicates the key findings.