Overview

Statistics and Data Visualization Using R: The Art and Practice of Data Analysis teaches students statistics visually, focusing on interpreting graphs and charts to learn statistical concepts, from the mean through regression.

Full Product Details

Author:   David S. Brown (University of Colorado, Boulder, USA)
Publisher:   SAGE Publications Inc
Imprint:   SAGE Publications Inc
Weight:   1.320kg
ISBN:  

9781544333861

ISBN 10:   1544333862
Pages:   616
Publication Date:   08 November 2021
Audience:   College/higher education ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active
Availability:   In stock  
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Table of Contents

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

Reviews

This book provides a well-written approach to beginning to intermediate-level statistical principles using the R statistical language. It provides some mathematical formulas to help students understand the underlying principles of statistics. It has many excellent social science examples. It provides the statistical understanding with a practical approach to using the most valuable statistical tool-R. Please consider it. I have been looking for a good social science textbook using R-this may be the best so far. -- Jeffrey D. Stone This text successfully presents an introduction to data analysis using R in a highly approachable manner. The use of easy-to-follow examples and conceptual linkage across chapters makes this an outstanding option for undergraduate and graduate stats courses in the social sciences. -- Joseph Nedelec A great text with in-depth coverage of statistics concepts with helpful R code segments. Great installation directions and rationale for use of R programming versus others. -- Esther Pearson This text takes students on a journey through introductory and intermediate statistical methods along with R programming to accomplish the descriptive and inferential statistics. Images of RStudio and samples of R code are woven throughout the text to help students follow along. -- Galen I. Papkov An accessible book for any student to learn data analysis, even without a strong math background. It is a student-friendly book that is easy to read, with knowledge checks as the student reads along, and there are great code examples and visualizations that will greatly engage the student. -- Catherine Garcia

Author Information

David Brown is a Professor and Divisional Dean of Social Sciences at the University of Colorado Boulder.

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