Package: statlingo 0.1.0

Introduction | Prerequisites | How statlingo Works: The explain() Function and ellmer | Understanding explain()'s Arguments | The Power of context: Why It Matters | Some Examples in Action! | Example 1: Linear Regression (lm) - Sales of Child Car Seats | 1. Model Appropriateness and Research Question | 2. Model Specification | 3. Residuals Summary | 4. Coefficients Table | 5. Residual Standard Error | 6. R-squared Values | 7. F-statistic | 8. Suggestions for Checking Assumptions | Caution | Follow-up Question: Interpreting R-squared | Example 2: Logistic GLM (glm) - Pima Indians Diabetes | Core Concepts & Purpose | Key Assumptions | Assessing Model Appropriateness | Interpretation of the GLM Output | Call / Model Specification | Deviance Residuals (Not provided in summary, but generally) | Coefficients Table | Dispersion Parameter | Deviance Statistics | AIC (Akaike Information Criterion) | Number of Fisher Scoring iterations | Suggestions for Checking Assumptions | Additional Considerations | Example 3: Cox Proportional Hazards Model (coxph) - Lung Cancer Survival | Cox Proportional Hazards Model Interpretation: Lung Cancer Survival | Key Findings | Overall Model Performance | Data Summary | Important Considerations for Next Steps | Example 4: Linear Mixed-Effects Model (lmer from lme4) - Sleep Study | Requesting Plain Text Output (style = "text") | Requesting JSON Output (style = "json") | Suggesting Next Coding Steps | Inspecting LLM Interaction | Conclusion