Model to Meaning: How to Interpret Statistical Models with R and Python

Cover....1 Half Title....2 Title Page....3 Copyright Page....4 Contents....5 Author....9 1. Who is this book for?....11 1.1. The big picture....11 1.2. Software....13 1.3. Documentation....14 1.4. Data....15 I. Interpretation....17 2. Models and meaning ....19 2.1. Why fit a model?....19 2.2. What is your estimand?....23 2.3. Making sense of parameter estimates....25 3. Conceptual framework ....27 3.1. Quantity....28 3.2. Predictors....33 3.3. Aggregation....39 3.4. Uncertainty....40 3.5. Test....41 3.6. Summary....42 II. Quantities and tests....47 4. Hypothesis and equivalence tests ....49 4.1. Null hypothesis....52 4.2. Equivalence....57 4.3. Summary....60 5. Predictions ....62 5.1. Quantity....63 5.2. Predictors....66 5.3. Aggregation....72 5.4. Uncertainty....75 5.5. Test....76 5.6. Visualization....80 5.7. Summary....86 6. Counterfactual comparisons ....89 6.1. Quantity....90 6.2. Predictors....95 6.3. Aggregation....103 6.4. Uncertainty....108 6.5. Test....108 6.6. Visualization....110 6.7. Summary....112 7. Slopes ....115 7.1. Quantity....116 7.2. Predictors....122 7.3. Aggregation....124 7.4. Uncertainty....125 7.5. Test....126 7.6. Visualization....127 7.7. Summary....129 III. Case studies....133 8. Causal inference with G-computation ....135 8.1. Treatment effects: ATE, ATT, ATU....136 8.2. Conditional treatment effects: CATE....144 9. Experiments ....146 9.1. Regression adjustment....146 9.2. Factorial experiments....148 10. Interactions and polynomials ....153 10.1. Multiplicative interactions....154 10.2. Polynomial regression....172 11. Categorical and ordinal outcomes ....177 11.1. Predictions....179 11.2. Counterfactual comparisons....183 12. Multilevel regression with poststratification ....185 12.1. Multilevel models....186 12.2. Frequentist....187 12.3. Bayesian....189 12.4. Poststratification....196 13. Machine learning ....200 13.1. tidymodels and mlr3....200 13.2. Predictions....202 13.3. Counterfactual comparisons....205 14. Uncertainty ....207 14.1. Delta method....207 14.2. Bootstrap....216 14.3. Simulation....218 14.4. Conformal prediction....220 IV. Back matter....229 Appendix I: Online content ....231 Appendix II: Python ....232 1. Who is this book for?....232 3. Conceptual framework....233 4. Hypothesis and equivalence tests....234 5. Predictions....235 6. Counterfactual comparisons....238 7. Slopes....242 8. Causal inference with G-computation....244 9. Experiments....245 10. Interactions and polynomials....246 13. Machine learning....248 Roadmap....250 Bibliography....251 Index....261