Statistics: The Art and Science of Learning from Data, Global Edition

Alan Agresti is a Distinguished Professor Emeritus in the Department of Statistics at the University of Florida. He taught statistics there for 38 years, including the development of e-courses in statistical methods for social science students and three courses in categorical data analysis. He is the author of more than 100 refereed articles and six texts, including Statistical Methods for the Social Sciences (Pearson, 5th edition, 2018) and An Introduction to Categorical Data Analysis (Wiley, 3rd edition, 2019). Alan has also received teaching awards from the University of Florida and an Excellence in Writing award from John Wiley & Sons. Christine Franklin is the K-12 Statistics Ambassador for the American Statistical Association and elected ASA Fellow. She has retired from the University of Georgia as the Lothar Tresp Honoratus Honors Professor and Senior Lecturer Emerita in Statistics. She is the co-author of two textbooks and has published more than 60 journal articles and book chapters. Chris was the lead writer for the American Statistical Association Pre-K-12 Guidelines for the Assessment and Instruction in Statistics Education (GAISE) Framework document, co-chair for the updated Pre-K-12 GAISE II, and chair of the ASA Statistical Education of Teachers (SET) report. Bernhard Klingenberg is a Professor of Statistics in the Department of Mathematics & Statistics at Williams College, where he has been teaching introductory and advanced statistics classes since 2004. He teaches statistical inference and modelling as well as data visualisation at the Graduate Data Science Program at New College of Florida. Prof. Klingenberg is responsible for the development of the web apps, which he programs using the R package Shiny. A native of Austria, he frequently returns there to hold visiting positions at universities and gives short courses on categorical data analysis in Europe and the United States. He also enjoys photography, with some of his pictures appearing in this book.

PART I: GATHERING AND EXPLORING DATA

Statistics: The Art and Science of Learning from Data

Using Data to Answer Statistical Questions
Sample Versus Population
Organizing Data, Statistical Software, and the New Field of Data Science
Chapter Summary
Chapter Exercises


Exploring Data with Graphs and Numerical Summaries

Different Types of Data
Graphical Summaries of Data
Measuring the Center of Quantitative Data
Measuring the Variability of Quantitative Data
Using Measures of Position to Describe Variability
Linear Transformations and Standardizing
Recognizing and Avoiding Misuses of Graphical Summaries
Chapter Summary
Chapter Exercises


Exploring Relationships Between Two Variables

The Association Between Two Categorical Variables
The Relationship Between Two Quantitative Variables
Linear Regression: Predicting the Outcome of a Variable
Cautions in Analyzing Associations
Chapter Summary
Chapter Exercises


Gathering Data

Experimental and Observational Studies
Good and Poor Ways to Sample
Good and Poor Ways to Experiment
Other Ways to Conduct Experimental and Nonexperimental Studies
Chapter Summary
Chapter Exercises



PART II: PROBABILITY, PROBABILITY DISTRIBUTIONS, AND SAMPLINGDISTRIBUTIONS

Probability in Our Daily Lives

How Probability Quantifies Randomness
Finding Probabilities
Conditional Probability
Applying the Probability Rules
Chapter Summary
Chapter Exercises


Random Variables and Probability Distributions

Summarizing Possible Outcomes and Their Probabilities
Probabilities for Bell-Shaped Distributions
Probabilities When Each Observation Has Two Possible Outcomes
Chapter Summary
Chapter Exercises


Sampling Distributions

How Sample Proportions Vary Around the Population Proportion
How Sample Means Vary Around the Population Mean
Using the Bootstrap to Find Sampling Distributions
Chapter Summary
Chapter Exercises



PART III: INFERENTIAL STATISTICS

Statistical Inference: Confidence Intervals

Point and Interval Estimates of Population Parameters
Confidence Interval for a Population Proportion
Confidence Interval for a Population Mean
Bootstrap Confidence Intervals
Chapter Summary
Chapter Exercises


Statistical Inference: Significance Tests About Hypotheses

Steps for Performing a Significance Test
Significance Tests About Proportions
Significance Tests About a Mean
Decisions and Types of Errors in Significance Tests
Limitations of Significance Tests
The Likelihood of a Type II Error
Chapter Summary
Chapter Exercises


Comparing Two Groups

Categorical Response: Comparing Two Proportions
Quantitative Response: Comparing Two Means
Comparing Two Groups with Bootstrap or Permutation Resampling
Analyzing Dependent Samples
Adjusting for the Effects of Other Variables
Chapter Summary
Chapter Exercises



PART IV: ANALYZING ASSOCIATION AND EXTENDED STATISTICALMETHODS

Analyzing the Association Between Categorical Variables

Independence and Dependence (Association)
Testing Categorical Variables for Independence
Determining the Strength of the Association
Using Residuals to Reveal the Pattern of Association
Fisher's Exact and Permutation Tests
Chapter Summary
Chapter Exercises


Analyzing the Association Between Quantitative Variables: Regression Analysis

Modeling How Two Variables Are Related
Inference About Model Parameters and the Association
Describing the Strength of Association
How the Data Vary Around the Regression Line
Exponential Regression: A Model for Nonlinearity
Chapter Summary
Chapter Exercises


Multiple Regression

Using Several Variables to Predict a Response
Extending the Correlation and R2 for Multiple Regression
Using Multiple Regression to Make Inferences
Checking a Regression Model Using Residual Plots
Regression and Categorical Predictors
Modeling a Categorical Response
Chapter Summary
Chapter Exercises


Comparing Groups: Analysis of Variance Methods

One-Way ANOVA: Comparing Several Means
Estimating Differences in Groups for a Single Factor
Two-Way ANOVA
Chapter Summary
Chapter Exercises


Nonparametric Statistics

Compare Two Groups by Ranking
Nonparametric Methods for Several Groups and for Matched Pairs
Chapter Summary
Chapter Exercises





Appendix
Answers
Index
Index of Applications
Credits