*For courses in introductory statistics.*

**The Art and Science of Learning from Data**

** ***Statistics: The Art and Science of Learning from Data,* Fourth Edition, takes a conceptual approach, helping students understand what statistics is about and learning the right questions to ask when analyzing data, rather than just memorizing procedures. This book takes the ideas that have turned statistics into a central science in modern life and makes them accessible, without compromising the necessary rigor. Students will enjoy reading this book, and will stay engaged with its wide variety of real-world data in the examples and exercises.

The authors believe that it’s important for students to learn and analyze both quantitative and categorical data. As a result, the text pays greater attention to the analysis of proportions than many other introductory statistics texts. Concepts are introduced first with categorical data, and then with quantitative data.

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**Alan Agresti** is Distinguished Professor Emeritus in the Department of Statistics at the University of Florida. He taught statistics there for 38 years and developed three courses in statistical methods for social science students and three courses in categorical data analysis. He is author of over 100 refereed articles and five texts including *Statistical Methods for the Social Sciences* (with Barbara Finlay, Prentice Hall, 4th edition 2009) and *Categorical Data Analysis* (Wiley, 2nd edition 2002). He is a Fellow of the American Statistical Association and recipient of an Honorary Doctor of Science from De Montfort University in the UK. In 2003, Alan was named "Statistician of the Year" by the Chicago chapter of the American Statistical Association and in 2004, he was the first honoree of the Herman Callaert Leadership Award in Biostatistical Education and Dissemination awarded by the University of Limburgs, Belgium. He has held visiting positions at Harvard University, Boston University, London School of Economics, and Imperial College and has taught courses or short courses for universities and companies in about 30 countries worldwide. Alan has also received teaching awards from the University of Florida and an excellence in writing award from John Wiley & Sons.

**Christine Franklin** is a Senior Lecturer and Lothar Tresp Honoratus Honors Professor in the Department of Statistics at the University of Georgia. She has been teaching statistics for more than 30 years at the college level. Chris has been actively involved at the national and state level with promoting statistical education in Pre-K—16 since the 1980s. She is a past Chief Reader for AP Statistics. She has developed three graduate-level courses at the University of Georgia in statistics for elementary, middle, and secondary teachers. Chris served as the lead writer for the ASA-endorsed *Guidelines for Assessment* *and Instruction in Statistics Education (GAISE) Report: A Pre-* *K—12 Curriculum Framework*.

Chris has been honored by her selection as a Fellow of the American Statistical Association, the 2006 Mu Sigma Rho National Statistical Education Award recipient for her teaching and lifetime devotion to statistics education, and numerous teaching and advising awards at the University of Georgia including election to the UGA Teaching Academy. Chris has written more than 50 journal articles and resource materials for textbooks.

**Bernhard Klingenberg **is Associate Professor of Statistics in the Department of Mathematics & Statistics at Williams College, where he has taught introductory and advanced statistics classes for more than 10 years. In 2013, Bernhard was instrumental in creating an undergraduate major in statistics at Williams, one of the first for a liberal arts college. At Williams, more than 70% of an incoming freshman class will have taken a course in introductory statistics by the time they graduate. 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 US. He has published several peer-reviewed articles in statistical journals and consults regularly with academia and industry. Bernhard enjoys photography (several of his pictures appear in this book), scuba diving, and spending time with his wife and four children.

Preface

**PART ONE: GATHERING AND EXPLORING DATA**

**1. Statistics: The Art and Science of Learning from Data**

1.1 Using Data to Answer Statistical Questions

1.2 Sample Versus Population

1.3 Using Calculators and Computers

Chapter Summary

Chapter Problems

**2. Exploring Data with Graphs and Numerical Summaries**

2.1 Different Types of Data

2.2 Graphical Summaries of Data

2.3 Measuring the Center of Quantitative Data

2.4 Measuring the Variability of Quantitative Data

2.5 Using Measures of Position to Describe Variability

2.6 Recognizing and Avoiding Misuses of Graphical Summaries

Chapter Summary

Chapter Problems

**3. Association: Contingency, Correlation, and Regression**

3.1 The Association Between Two Categorical Variables

3.2 The Association Between Two Quantitative Variables

3.3 Predicting the Outcome of a Variable

3.4 Cautions in Analyzing Associations

Chapter Summary

Chapter Problems

**4. Gathering Data**

4.1 Experimental and Observational Studies

4.2 Good and Poor Ways to Sample

4.3 Good and Poor Ways to Experiment

4.4 Other Ways to Conduct Experimental and Nonexperimental Studies

Chapter Summary

Chapter Problems

Part Review 1 (ONLINE)

**PART TWO: PROBABILITY, PROBABILITY DISTRIBUTIONS, AND SAMPLING DISTRIBUTIONS**

**5. Probability in Our Daily Lives**

5.1 How Probability Quantifies Randomness

5.2 Finding Probabilities

5.3 Conditional Probability

5.4 Applying the Probability Rules

Chapter Summary

Chapter Problems

**6. Probability Distributions**

6.1 Summarizing Possible Outcomes and Their Probabilities

6.2 Probabilities for Bell-Shaped Distributions

6.3 Probabilities When Each Observation Has Two Possible Outcomes

Chapter Summary

Chapter Problems

**7. Sampling Distributions**

7.1 How Sample Proportions Vary Around the Population Proportion

7.2 How Sample Means Vary Around the Population Mean

Chapter Summary

Chapter Problems

Part Review 2 (ONLINE)

**PART THREE: INFERENTIAL STATISTICS**

**8. Statistical Inference: Confidence Intervals**

8.1 Point and Interval Estimates of Population Parameters

8.2 Constructing a Confidence Interval to Estimate a Population Proportion

8.3 Constructing a Confidence Interval to Estimate a Population Mean

8.4 Choosing the Sample Size for a Study

8.5 Using Computers to Make New Estimation Methods Possible

Chapter Summary

Chapter Problems

**9. Statistical Inference: Significance Tests About Hypotheses**

9.1 Steps for Performing a Significance Test

9.2 Significance Tests About Proportions

9.3 Significance Tests About Means

9.4 Decisions and Types of Errors in Significance Tests

9.5 Limitations of Significance Tests

9.6 The Likelihood of a Type II Error

Chapter Summary

Chapter Problems

**10. Comparing Two Groups**

10.1 Categorical Response: Comparing Two Proportions

10.2 Quantitative Response: Comparing Two Means

10.3 Other Ways of Comparing Means and Comparing Proportions

10.4 Analyzing Dependent Samples

10.5 Adjusting for the Effects of Other Variables

Chapter Summary

Chapter Problems

Part Review 3 (ONLINE)

**PART FOUR: ANALYZING ASSOCIATION AND EXTENDED STATISTICAL METHODS**

**11. Analyzing the Association Between Categorical Variables**

11.1 Independence and Dependence (Association)

11.2 Testing Categorical Variables for Independence

11.3 Determining the Strength of the Association

11.4 Using Residuals to Reveal the Pattern of Association

11.5 Fisher’s Exact and Permutation Tests

Chapter Summary

Chapter Problems

**12. Analyzing the Association Between Quantitative Variables: Regression Analysis**

12.1 Modeling How Two Variables Are Related

12.2 Inference About Model Parameters and the Association

12.3 Describing the Strength of Association

12.4 How the Data Vary Around the Regression Line

12.5 Exponential Regression: A Model for Nonlinearity

Chapter Summary

Chapter Problems

**13. Multiple Regression**

13.1 Using Several Variables to Predict a Response

13.2 Extending the Correlation and R2 for Multiple Regression

13.3 Using Multiple Regression to Make Inferences

13.4 Checking a Regression Model Using Residual Plots

13.5 Regression and Categorical Predictors

13.6 Modeling a Categorical Response

Chapter Summary

Chapter Problems

**14. Comparing Groups: Analysis of Variance Methods**

14.1 One-Way ANOVA: Comparing Several Means

14.2 Estimating Differences in Groups for a Single Factor

14.3 Two-Way ANOVA

Chapter Summary

Chapter Problems

**15. Nonparametric Statistics**

15.1 Compare Two Groups by Ranking

15.2 Nonparametric Methods for Several Groups and for Matched Pairs

Chapter Summary

Chapter Problems

Part Review 4 (ONLINE)

Tables

Answers

Index

Index of Applications

Photo Credits