图书简介

Preface \\ Acknowledgments \\ About the Author \\ Chapter 1: Getting Started \\ Learning Objectives \\ Overview \\ R, RStudio, and R Markdown \\ Objects and Functions \\ Getting Started in RStudio \\ Navigating RStudio With R Markdown \\ Using R Markdown Files Versus R-Scripts \\ A Little Practice \\ Summary \\ Common Problems \\ Review Questions \\ Practice on Analysis and Visualization \\ Annotated R Functions \\ Answers \\ Chapter 2: An Introduction to Data Analysis \\ Learning Objectives \\ Overview \\ Motivating Data Analysis \\ The Main Components of Data Analysis \\ Developing Hypotheses by Describing Data \\ Model Building and Estimation \\ Diagnostics \\ Next Questions \\ Summary \\ Common Problems \\ Review Questions \\ Practice on Analysis and Visualization \\ Annotated R Functions \\ Answers \\ Chapter 3: Describing Data \\ Learning Objectives \\ Overview \\ Data Sets and Variables \\ Different Kinds of Variables \\ Describing Data Saves Time and Effort \\ Measurement \\ Summary \\ Common Problems \\ Review Questions \\ Practice on Analysis and Visualization \\ Annotated R Functions \\ Answers \\ Chapter 4: Central Tendency and Dispersion \\ Learning Objectives \\ Overview \\ Measures of Central Tendency: The Mode, Mean, and Median \\ Mean Versus Median \\ Measures of Dispersion: The Range, Interquartile Range, and Standard Deviation \\ Interquartile Range Versus Standard Deviation \\ Summary \\ Common Problems \\ Review Questions \\ Practice on Analysis and Visualization \\ Annotated R Functions \\ Answers \\ Chapter 5: Univariate and Bivariate Descriptions of Data \\ Learning Objectives \\ Overview \\ The Good, the Bad, and the Outlier \\ Five Views of Univariate Data \\ Are They in a Relationship? \\ Summary \\ Common Problems \\ Review Questions \\ Practice on Analysis and Visualization \\ Annotated R Functions \\ Answers \\ Chapter 6: Transforming Data \\ Learning Objectives \\ Overview \\ Theoretical Reasons for Transforming Data \\ Transforming Data for Practical Reasons \\ Transforming Data—Continuous to Categorical Variables \\ Transforming Data—Changing Categories \\ Box-Cox Transformations \\ Summary \\ Common Problems \\ Review Questions \\ Practice on Analysis and Visualization \\ Annotated R Functions \\ Answers \\ Chapter 7: Some Principles of Displaying Data \\ Learning Objectives \\ Overview \\ Some Elements of Style \\ The Basic Elements of a Story \\ Documentation (Establishing Credibility as a Storyteller) \\ Build an Intuition (Setting the Context) \\ Show Causation (The Journey) \\ From Causation to Action (The Resolution) \\ Summary \\ Common Problems \\ Review Questions \\ Practice on Analysis and Visualization \\ Annotated R Functions \\ Answers \\ Chapter 8: The Essentials of Probability Theory \\ Overview \\ Learning Objectives \\ Populations and Samples \\ Sample Bias and Random Samples \\ The Law of Large Numbers \\ The Central Limit Theorem \\ The Standard Normal Distribution \\ Summary \\ Common Problems \\ Review Questions \\ Practice on Analysis and Visualization \\ Annotated R Functions \\ Answers \\ Chapter 9: Confidence Intervals and Testing Hypotheses \\ Learning Objectives \\ Overview \\ Confidence Intervals With Large Samples \\ Small Samples and the t-Distribution \\ Comparing Two Sample Means \\ Confidence Levels \\ A Brief Note on Statistical Inference and Causation \\ Summary \\ Common Problems \\ Review Questions \\ Practice on Analysis and Visualization \\ Annotated R Functions \\ Answers \\ Chapter 10: Making Comparisons \\ Overview \\ Learning Objectives \\ Why Do We Make Comparisons? \\ Questions That Beg Comparisons \\ Comparing Two Categorical Variables \\ Comparing Continuous and Categorical Variables \\ Comparing Two Continuous Variables \\ Exploratory Data Analysis: Investigating Abortion Rates in the United States \\ Good Analysis Generates Additional Questions \\ Summary \\ Common Problems \\ Review Questions \\ Practice on Analysis and Visualization \\ Annotated R Functions \\ Answers \\ Chapter 11: Controlled Comparisons \\ Learning Objectives \\ Overview \\ What Is a Controlled Comparison? \\ Comparing Two Categorical Variables, Controlling for a Third \\ Comparing Two Continuous Variables, Controlling for a Third \\ Arguments and Controlled Comparisons \\ Summary \\ Common Problems \\ Review Questions \\ Practice on Analysis and Visualization \\ Annotated R Functions \\ Answers \\ Practice on Analysis and Visualization \\ Chapter 12: Linear Regression \\ Learning Objectives \\ Overview \\ The Advantages of Linear Regression \\ The Slope and Intercept in Linear Regression \\ Goodness of Fit (R2 Statistic) \\ Statistical Significance \\ Examples of Bivariate Regressions \\ Summary \\ Common Problems \\ Review Questions \\ Practice on Analysis and Visualization \\ Annotated R Functions \\ Answers \\ Chapter 13: Multiple Regression \\ Learning Objectives \\ Overview \\ What Is Multiple Regression? \\ Regression Models and Arguments \\ Regression Models, Theory, and Evidence \\ Interpreting Estimates in Multiple Regression \\ Example: Homicide Rate and Education \\ Summary \\ Common Problems \\ Review Questions \\ Practice on Analysis and Visualization \\ Annotated R Functions \\ Answers \\ Practice on Analysis and Visualization \\ Chapter 14: Dummies and Interactions \\ Learning Objectives \\ Overview \\ What Is a Dummy Variable? \\ Additive Models and Interactive Models \\ Bivariate Dummy Variable Regression \\ Multiple Regression and Dummy Variables \\ Interactions in Multiple Regression \\ Summary \\ Common Problems \\ Review Questions \\ Practice on Analysis and Visualization \\ Annotated R Functions \\ Answers \\ Chapter 15: Diagnostics I: Is Ordinary Least Squares Appropriate? \\ Learning Objectives \\ Overview \\ Diagnostics in Regression Analysis \\ Properties of Statistics and Estimators \\ The Gauss-Markov Assumptions \\ The Residual Plot \\ Summary \\ Common Problems \\ Review Questions \\ Practice on Analysis and Visualization \\ Annotated R Functions \\ Answers \\ Chapter 16: Diagnostics II: Residuals, Leverages, and Measures of Influence \\ Learning Objectives \\ Overview \\ Outliers \\ Leverages \\ Measures of Influence \\ Added Variable Plots \\ Summary \\ Common Problems \\ Review Questions \\ Practice on Analysis and Visualization \\ Annotated R Functions \\ Answers \\ Chapter 17: Logistic Regression \\ Learning Objectives \\ Overview \\ Questions and Problems That Require Logistic Regression \\ Logistic Regression Violates Gauss-Markov Assumptions \\ Working With Logged Odds \\ Working With Predicted Probabilities \\ Model Fit With Logistic Regression \\ Summary \\ Common Problems \\ Review Questions \\ Practice on Analysis and Visualization \\ Annotated R Functions \\ Answers \\ Appendix: Developing Empirical Implications \\ Overview \\ Developing Empirical Implications \\ Testing Additional Dependent Variables \\ Testing Additional Independent Variables \\ Using Information on Cases \\ Causal Mechanisms \\ The Rabbit Hole \\ Glossary \\ References \\ Index

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