Statistics: The Art and Science of Learning from Data: International Edition (3e)

Statistics: The Art and Science of Learning from Data, Third Edition, helps students become statistically literate by encouraging them to ask and answer interesting statistical questions. This book takes the ideas that have turned statistics into a central science in modern life and makes them accessible without compromising necessary rigor. Authors Alan Agresti and Christine Franklin 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.

The Third Edition has been edited for conciseness and clarity to keep students focused on the main concepts. The data-rich examples that feature intriguing human-interest topics now include topic labels to indicate which statistical topic is being applied. New learning objectives for each chapter appear in the Instructor’s Edition, making it easier to plan lectures and Chapter 7 (Sampling Distributions) now incorporates simulations in addition to the mathematical formulas.

Part 1: 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 1 Review

Part 1 Questions

Part 1 Exercises

Part 2: 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: The Probability of A Given B

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

7.3 The Binomial Distribution Is a Sampling Distribution (Optional)

Chapter Summary

Chapter Problems

Part 2 Review

Part 2 Questions

Part 2 Exercises

Part 3: 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 (Not Rejecting H₀, Even Though It’s False)

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 3 Review

Part 3 Questions

Part 3 Exercises

Part 4: Analyzing Association and Extended Statistical Methods

11. Analyzing the Association Between Categorical Variables

11.1 Independence and 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 Small Sample Sizes: Fisher’s Exact Test

Chapter Summary
Chapter Problems

12. Analyzing the Association Between Quantitative Variables: Regression Analysis

12.1 Model How Two Variables Are Related

12.2 Describe Strength of Association

12.3 Make Inference About the Association

12.4How 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 R-squared 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 4 Review

Part 4 Questions

Part 4 Exercises

Tables

Answers

Index

Index of Applications

Photo Credits

Content Updates include:

• 25% new and updated exercises ensure students have ample opportunity to practice techniques and apply their knowledge.
• 25% new and updated examples keep this edition relevant for today’s students.
• The General Social Survey data has been updated to the most recent survey results.
• Chapter 7, Sampling Distributions, has been rewritten extensively to focus on emphasizing simulation to develop the concepts of sampling distribution rather than the more traditional mathematical approach.  This chapter is now better organized to distinguish a population, data and sampling, and more effectively prepares students for standard error terminology, which is introduced in Chapter 8.

Added Student and Instructor Support

• The chapters have been heavily edited for clarity to help students better grasp the chapter concepts.
• The book’s design has been refined to facilitate navigating the chapter material.
• Caution boxes now appear at appropriate places in the margins to help students avoid common mistakes.
• Each example includes a concept label to identify the topic being covered, which helps students when reviewing material and helps instructors when preparing lectures.
• Learning objectives are listed in the instructor notes in the Instructor’s Edition, adding to the wealth of instructor-to-instructor advice from the authors.

MyStatLab exercise coverage is increased to 50%; two new question libraries are available to draw from, and there are new statistical software resources for students.

• Technology Instruction Videos provide step-by-step instructions on how to perform statistical procedures using Excel^®, Minitab^®, SPSS, and the TI Graphing Calculator.
• StatCrunch is integrated within the eBook. With a single click, the data set on the page opens in StatCrunch, allowing point-of-use data analysis.

• Conceptual Question Library: In addition to algorithmically regenerated questions that are aligned with the textbook, there is a library of 1,000 Conceptual Questions available that focus on student understanding of statistical concepts.
• Getting Ready for Statistics: A library of questions focusing on developmental math topics is available. These can be assigned as a prerequisite to other assignments, if desired.

• Promoting Student Learning: these features were created to motivate students to think about the material presented, ask interesting questions, and develop good problem-solving skills.
• In Words summarizes complicated symbolic notation and formal definitions in a non-technical, less formal way to help students understand “what it really means.”
• New Caution margin boxes appear at appropriate places to help students avoid common mistakes.
• Recall margin boxes direct students back to previous presentations in the text to review definitions and formulas and to reinforce key concepts in context.
• Did You Know margin boxes provide information that helps with the contextual understanding of the statistical questions.
• Annotated figures feature labels to identify noteworthy aspects in each illustration that may not be obvious to inexperienced students; many captions include questions designed to challenge students to think more about the information being presented.
• Active learning is encouraged through the use of simulations and hands-on activities. Activities and exercises throughout the textbook and companion CD-ROM direct students to the applets, the Internet, and other resources.

• Real World Connections
• Chapter opening examples include a high-interest example that raises questions and establishes a chapter theme. Opening examples use real-world data from a variety of applications.
• In Practice boxes alert students to the way statisticians actually analyze data in the real world.
• Exercises and Examples incorporate real data and focus on intriguing topics that appeal to students.
• Examples emphasize thinking about and understanding statistics through analysis of current real data. The unique five-step format encourages students to model the thought processes required to examine issues in statistics.

• Picture the Scenario presents background information so that students can understand the context of the data.
• Questions to Explore show students the appropriate questions to ask about the scenario and focus on what students should learn from the example.
• Think It Through is the heart of each example, as the Questions to Explore are investigated and answered using the appropriate statistical methods.
• Insight clarifies the central ideas investigated in the example and places them in a broader context that states the conclusions in less technical terms. This step also connects concepts from other sections in the book.
• Try Exercise  directs students to an end-of-section exercise that allows immediate practice of the concept.

• Concept tags are included with each example so that students can easily identify the concept demonstrated in the example.
• A plethora of chapter exercises test student comprehension through interesting real-data problems. Exercises, divided into three categories, address relevant and through-provoking topics, such as cell phone usage and cancer and public support for the death penalty.

• Practicing the Basics reinforce basic applications of methods.
• Concepts and Investigations require students to explore the theory and concepts presented in the chapter through real data sets.
• Student Activities are appropriate for individual or group work and often make use of the applets that accompany the text.
• “Part” organization divides the book into four Parts. Each Part concludes with a substantive Part Review to help students understand the “big picture” and solve exercises that review the key concepts, ideas, and techniques. Included are Summary Questions, Summary Examples, and Part Exercises.

• Technology Integration

• Modern technological techniques are used to develop concepts and analyze data. Output from computer software (Minitab^®, SPSS^®, Microsoft Excel^®) and the TI-83/84 Plus graphing calculator are used to illustrate many concepts.
• Activities and Applets referred to in the text are found on the companion CD-ROM that accompanies the text and within MyStatLab.  The use of Applets offers a way to show students certain concepts visually.
• Book-specific technology manuals that contain detailed tutorial instructions and worked-out examples and exercises are available for Minitab, Excel and the Graphing Calculator. They are available within MyStatLab or download from www.pearsonhighered.com/mathstatsresources

• Helpful instructor features

• Chapter-specific Instructor Notes appear in the Annotated Instructor’s Edition. These time-saving notes give insights into the authors’ approach to the material, suggestions for additional classroom examples and activities, and other helpful teaching tips. Learning objectives are new to this edition.
• Instructor-to-Instructor videos feature Chris Franklin giving her perspective on the chapter and helpful suggestions for how to teach from the book.  The videos are available for download from Pearson’s Instructor Resource Center.

Alan Agresti is Distinguished Professor Emeritus in the Department of Statistics at the University of Florida. He taught statistics there for 38 years, including the development of 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 UF 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 at Pre-K–16 since the 1980s. She is a past Chief Reader for AP Statistic. 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.