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

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September 2022

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Statistics: The Art and Science of Learning from Data, Global Edition
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Introduce your students to the art and science of learning from data.

Statistics: The Art and Science of Learning from Data, Global Edition, 5th edition is the ideal introduction to statistics, encouraging students to analyse data the right way by enquiring and searching for the right questions and information rather than just memorising procedures.


Hallmark features of this title

Encourages your students to think about the data by asking the right questions.
  • With its structured approach, the text provides examples and problems that will prompt your students to think and frame the right questions when carrying out data analysis.
  • Presents a wide range of data examples and exercises inspired by the world around us.
An approachable style and language that is easy to understand
  • A simplified text and prose address your students' needs without compromising the necessary academic rigour.
  • Helps students learn at an introductory level from theory to practice, with a clear presentation of the concepts and a balance of quantitative and categorical data to work with.

New to this Edition

New and updated features of this title

New and updated content reflects the importance of the statistical investigative process in data analysis.
  • The updated content in Chapter 1 offers an additional introduction to the opportunities and challenges with Big Data and Data Science, including a discussion of ethical considerations.
  • A new section in Chapter 2 refers to the main features of linear transformations.
  • There is further emphasis on the two descriptive statistics, most likely encountered by students in their daily lives (differences and ratios of proportions) in Section 3.1.
  • An expanded discussion on multivariate thinking is presented in Section 3.3.
  • A significantly expanded coverage of resampling methods, with a thorough discussion of the bootstrap for one and two-sample problems and the correlation coefficient, in new Sections 7.3, 8.3, and 10.3.
  • Continued emphasis on using interval estimation for inference and less reliance on significance testing incorporates the 2016 American Statistical Association’s statement on P-values.
  • A new section on statistical software at the end of each chapter provides commented R code, showing students how the analysis can be replicated and carried out in the statistical software R.
  • Many new and updated featured examples and exercises use the most recent data available.

Table of Contents


  1. 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
  2. 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
  3. 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
  4. 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

  6. Probability in Our Daily Lives
    • How Probability Quantifies Randomness
    • Finding Probabilities
    • Conditional Probability
    • Applying the Probability Rules
    Chapter Summary
    Chapter Exercises
  7. 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
  8. 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

  10. 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
  11. 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
  12. 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


  14. 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
  15. 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
  16. 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
  17. 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
  18. Nonparametric Statistics
    • Compare Two Groups by Ranking
    • Nonparametric Methods for Several Groups and for Matched Pairs
    Chapter Summary
    Chapter Exercises
Index of Applications


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 also teaches statistical inference and modelling as well as data visualisation at the Graduate Data Science Program at New College of Florida.

Bernhard is responsible for the development of the web apps, which he programs using the R package Shiny. A native of Austria, Bernhard frequently returns there to hold visiting positions at universities and gives short courses on categorical data analysis in Europe and the United States.

Bernhard also enjoys photography, with some of his pictures appearing in this book.