Statistics: The Art and Science of Learning from Data, 4th edition
Published by Pearson (January 3, 2016) © 2017
- Alan Agresti University of Florida
- Christine A. Franklin University of Georgia
- Bernhard Klingenberg
MyLab
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- NEW! Topical coverage reflecting the latest trends in statistical education, including:
- Measures of association for categorical variables in Chapter 3
- Permutation testing in Chapters 10 and 11
- Updated coverage of McNemar's test in Chapter 10 (previously Chapter 11)
- 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 via Learning Catalytics.
- Learning Catalytics is a web-based engagement and assessment tool. As a "bring-your-own-device" direct response system, Learning Catalytics offers a diverse library of dynamic question types that allow students to interact with and think critically about statistical concepts. As a real-time resource, instructors can take advantage of critical teaching moments both in the classroom or through assignable and gradable homework.
- 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.
- A 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.
- NEW and updated example videos are available in MyStatLab for students to watch as they work through in-text examples.
- A plethora of chapter exercises test student comprehension through interesting real-data problems. Exercises, divided into three categories, address relevant and thought-provoking topics, such as cell phone usage, 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 web apps that accompany the text.
- “Part” organization divides the book into four Parts. Each Part has a corresponding Part Review in MyStatLab 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®, StatCrunch, Microsoft Excel®) and the TI-83/84 Plus graphing calculator is used to illustrate many concepts.
- Activities and Web Apps referred to in the text are found within MyStatLab or at the Companion Website (www.pearsonhighered.com/AFK). The use of web apps offers a way to show students certain concepts visually.
- 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, learning objectives, and other helpful teaching tips.
- UPDATED! Instructor-to-Instructor videos feature the authors’ perspectives on chapters and helpful suggestions for how to teach from the book. The videos can be accessed through a link in Pearson’s Instructor Resource Center and through MyStatLab.
Also available with MyStatLab
MyStatLab™ is an online homework, tutorial, and assessment program designed to work with this text to engage students and improve results. Within its structured environment, students practice what they learn, test their understanding, and pursue a personalized study plan that helps them absorb course material and understand difficult concepts. For this edition, new web apps with complementary exercises, a tightly integrated video program, and strong exercise coverage enhance student learning.
- NEW! Increased exercise coverage on 60% of the book's exercises, gives instructors even more options when creating assignments.
- NEW! Web Apps—delivered through examples, exercises, and simulations—allow students to interact with key statistical concepts and techniques, including permutation tests, bootstrapping, and sampling distributions. Students can explore the consequences of changing parameters and carry out statistical inference.
- UPDATED! Technology Instruction Videos provide step-by-step instructions on how to perform statistical procedures using Excel®, Minitab®, StatCrunch, 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 also a library of 1,000 Conceptual Questions 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.
- NEW! Learning Catalytics is a web-based engagement and assessment tool. As a "bring-your-own-device" direct response system, Learning Catalytics offers a diverse library of dynamic question types that allow students to interact with and think critically about statistical concepts. As a real-time resource, instructors can take advantage of critical teaching moments both in the classroom or through assignable and gradable homework.
- NEW! Topical coverage reflecting the latest trends in statistical education, including:
- Measures of association for categorical variables in Chapter 3
- Permutation testing in Chapters 10 and 11
- Updated coverage of McNemar's test in Chapter 10 (previously Chapter 11)
- New Caution margin boxes appear at appropriate places to help students avoid common mistakes.
Also available with MyStatLab
MyStatLab™ is an online homework, tutorial, and assessment program designed to work with this text to engage students and improve results. Within its structured environment, students practice what they learn, test their understanding, and pursue a personalized study plan that helps them absorb course material and understand difficult concepts. For this edition, new web apps with complementary exercises, a tightly integrated video program, and strong exercise coverage enhance student learning.
- Increased exercise coverage on 60% of the book's exercises, gives instructors even more options when creating assignments.
- Web Apps—delivered through examples, exercises, and simulations—allow students to interact with key statistical concepts and techniques, including permutation tests, bootstrapping, and sampling distributions. Students can explore the consequences of changing parameters and carry out statistical inference.
- UPDATED! Technology Instruction Videos provide step-by-step instructions on how to perform statistical procedures using Excel®, Minitab®, StatCrunch, and the TI Graphing Calculator.
- UPDATED! Instructor-to-Instructor videos feature the authors’ perspectives on chapters and helpful suggestions for how to teach from the book. The videos can be accessed through a link in Pearson’s Instructor Resource Center and through MyStatLab.
- Learning Catalytics is a web-based engagement and assessment tool. As a "bring-your-own-device" direct response system, Learning Catalytics offers a diverse library of dynamic question types that allow students to interact with and think critically about statistical concepts. As a real-time resource, instructors can take advantage of critical teaching moments both in the classroom or through assignable and gradable homework.
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 CreditsAlan 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 (Chris) Franklin is the K-12 Statistics Ambassador for the American Statistical Association and an elected ASA Fellow. She is 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 an Introductory Statistics textbook for post secondary, co-author for a sports statistics textbook for high school, and has published more than 60 journal articles and book chapters. Chris was the lead writer for the groundbreaking document of the American Statistical Association Pre-K-12 Guidelines for the Assessment and Instruction in Statistics Education (GAISE) Framework and chaired the writing team of the ASA Statistical Education of Teachers (SET) report. She is a past Chief Reader for Advance Placement Statistics, a Fulbright scholar to New Zealand (2015), recipient of the United States Conference on Teaching Statistics (USCOTS) Lifetime Achievement Award, the prestigious ASA Founder’s award and an elected member of the International Statistical Institute (ISI). Chris loves running, hiking, scoring baseball games, and reading mysteries.
Bernhard Klingenberg is a 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.
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