Summary | Statement of Need | Introduction | Regression analysis the "good old-fashioned" way | Regression analysis using moderndive | Bonus: Visualizing parallel slopes models with moderndive | Why should you use the moderndive package? | Features | 1. Less p-value stars, more confidence intervals | 2. Outputs as tibbles | 3. Birds of a feather should flock together: Fitted values & residuals | 4. A quick-and-easy Kaggle predictive modeling competition submission! | 5. Scalar summaries of linear regression model fits | 6. Plot parallel slopes regression models | The Details | Three wrappers to broom functions | Custom geometries | Author contributions | Acknowledgments | References

Acknowledgment | Note on large datasets | All datasets | Motivation | "Tame" data principles

Abstract | Introduction | Educational Landscape | Two Conflicting Goals | The fivethirtyeight R Package | Basic Usage | Example Usage | Vignettes | Package Source Code | "Tame Data" Principles for Introductory Statistics and Data Science Courses | Naming Conventions | Dates | Factors vs Characters | Tidy Data Format | Conclusion | The Future | Biographies | Acknowledgments | References

Purpose | Example analysis | Compute growth of trees based on census data | Add spatial information | Compute focal versus competitor tree information | Fit model and make predictions | Run spatial cross-validation

Summary | Statement of Need | Introduction | Regression analysis the "good old-fashioned" way | Regression analysis using moderndive | Repository README | Author contributions | Acknowledgments | References