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Statistics and Data Visualization Using R
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Statistics and Data Visualization Using R
The Art and Practice of Data Analysis



September 2021 | 616 pages | SAGE Publications, Inc
Designed to introduce students to quantitative methods in a way that can be applied to all kinds of data in all kinds of situations, Statistics and Data Visualization Using R: The Art and Practice of Data Analysis by David S. Brown teaches students statistics through charts, graphs, and displays of data that help students develop intuition around statistics as well as data visualization skills. By focusing on the visual nature of statistics instead of mathematical proofs and derivations, students can see the relationships between variables that are the foundation of quantitative analysis. Using the latest tools in R and R RStudio® for calculations and data visualization, students learn valuable skills they can take with them into a variety of future careers in the public sector, the private sector, or academia. Starting at the most basic introduction to data and going through most crucial statistical methods, this introductory textbook quickly gets students new to statistics up to speed running analyses and interpreting data from social science research.

 
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

Supplements

Instructor Website
edge.sagepub.com/brownstats1e


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Student Website

edge.sagepub.com/brownstats1e

 

The open-access Student Study Site makes it easy for students to maximize their study time, anywhere, anytime. It offers datasets and code for use in R.

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
California State University Los Angeles

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
University of Cincinnati

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
Lasell University

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
Florida Gulf Coast University

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
University of Nebraska - Lincoln

David Stacy Brown

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

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ISBN: 9781544333861
$135.00