# Intro Stats, 5th edition

Published by Pearson (August 25, 2017) © 2018

**Richard D. De Veaux**Williams College**Paul F. Velleman**Cornell University**David E. Bock**Ithaca High School (Retired) , Cornell University

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## MyLab

- Reach every student with personalized support
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- Optimize learning with dynamic study tools

__Reflects the new Guidelines for Assessment and Instruction in Statistics Education (GAISE) 2016 report adopted by the American Statistical Association to encourage statistical thinking__

**NEW! Random Matters:**This new feature encourages a gradual, cumulative understanding of randomization. The first Random Matters box introduces drawing inferences from data. Subsequent Random Matters features draw histograms of sample means, introduce the thinking involved in permutation tests, and encourage judgment about how likely the observed statistic seems when viewed against the simulated sampling distribution of the null hypothesis.**NEW! Streamlined coverage of descriptive statistics**helps students progress more quickly through the first part of the book. Also a GAISE recommendation, random variables and probability distributions are now covered later in the text to allow for more time on the more critical statistical concepts.**NEW! Technology is utilized to improve the learning of two of the most difficult concepts in the introductory course**: the idea of a sampling distribution and the reasoning of statistical inference.**NEW! A third variable is introduced with contingency tables and mosaic plots in Chapter 3**to give students earlier experience with multivariable thinking. Then, following the discussion of correlation and regression as a tool (without inference) in Chapters 6, 7, and 8, multiple regression is introduced in Chapter 9.**Where Are We Going? chapter openers**give a context for the work students are about to begin within the broader course.**Margin and in-text boxed notes**throughout each chapter enhance and enrich the text.**Reality Checks**ask students to think about whether their answers make sense before interpreting their results.**Notation Alerts**appear whenever special notation is introduced.

The

**Tech Support**section provides instructions for applying the topics covered by the chapter within each of the supported statistics packages.

__Supports learning through worked examples and practice opportunities__

**UPDATED! Expanded and revised**guide students through the process of analyzing a problem through worked examples. They illustrate the importance of thinking about a statistics question (Think) and reporting findings (Tell)). The Show step contains the mechanics of calculating results. This results in a better understanding of the concept and problem-solving process that goes beyond number crunching.*Think, Show, Tell examples***Focused examples**are provided as each important concept is introduced, applying the concept usually with real, up-to-the-minute data. Many examples carry the discussion through the chapter, picking up the story and moving it forward as students learn more about the topic.**Just Checking questions**are quick checks throughout the chapter that involve minimal calculation and encourage students to pause and think about what they’ve just read. The Just Checking answers are at the end of the exercise sets in each chapter so students can easily check themselves.**End-of-chapter material**includes:**Connections**sections that specifically ties the new topics to those learned in previous chapters.**What Can Go Wrong?**sections that highlight the most common errors that people make and the misconceptions they have about statistics. One of our goals is to arm students with the tools to detect statistical errors and to offer practice in debunking misuses of statistics., whether intentional or not.**Chapter Reviews**that summarizes the story told by the chapter and provides a bulleted lists of the major concepts and principles covered.A

**Review of Terms**glossary of all of the boldfaced terms introduced in the chapter including. The Review provides page references, so students can easily turn back to a full discussion of the term if the brief definition isn’t sufficient.

**Abundant exercises at the end of each chapter**start with relatively simple, focused exercises for each chapter section and move on to more extensive exercises that may deal with topics from several parts of the chapter or even from previous chapters as they combine with the topics of the chapter at hand. All exercises appear in pairs, and odd-numbered exercises have answers in the back of student texts. Each even-numbered exercise covers the same topic (although not in exactly the same way) as the previous odd exercise.**More than 600 of the exercises include an icon indicating that the dataset referenced is available electronically.**The exercise title or a note provides the dataset title. Some exercises are tagged to indicate that they call for the student to generate random samples or use randomization methods such as the bootstrap.

**Part Reviews**discuss the concepts in each part of the text, tying them together and summarizing the material.**Additional exercises**follow the Part Reviews; these are not paired and not tied to a chapter, making them more like potential exam questions and a good tool for review.**Parts I-V Cumulative Review Exercises**comprise a final book-level review section towards the end of the text. Cumulative Review exercises are longer and cover concepts from the book as a whole.

Most of the text’s supporting materials can be found online at the book’s websiteor within the MyLab Statistics course.

Datasets are also available at dasl.datadesk.com.

The Data Desk statistics program is available from datadesk.com.

**NEW! Web tools**that provide interactive versions of the distribution tables at the back of the book and tools for randomization inference methods such as the bootstrap and for repeated sampling from larger populations can be found online at astools.datadesk.com.

**Also available with MyLab Statistics**

MyLab™ Statistics is the teaching and learning platform that empowers you to reach every student. By combining trusted author content with digital tools and a flexible platform, MyLab Statistics personalizes the learning experience and improves results for each student. With MyLab Statistics and StatCrunch, an integrated web-based statistical software program, students learn the skills they need to interact with data in the real world. Learn more about MyLab Statistics.

__Preparedness__

Getting Ready for Statistics Questions:This question library contains more than 450 exercises that cover the relevant developmental math topics for a given section. These can be made available to students for extra practice or assigned as a prerequisite to other assignments.

__Conceptual Understanding__

StatCrunch^{®}: This powerful, web-based statistical software is integrated into MyLab Statistics, so students can quickly and easily analyze any data set, including those from their text and MyLab Statistics exercises. In addition, MyLab Statistics includes access to www.StatCrunch.com, a web-based community where users can access tens of thousands of shared data sets, create and conduct online surveys, pull data from almost any web page, interact with a full library of applets, and perform complex analyses using the powerful statistical software.Technology-Specific Video Tutorialsaddress how to use different technologies to complete exercises.Technology-Specific Study Cardsprovide students with instructional support when using a variety of statistical software programs including, StatCrunch, Excel^{®}, Minitab, JMP, R, SPSS, and TI 83/84 calculators.Data sets from MyLab Statistics exercisesand from the textbook are available to download into software such as StatCrunch or Excel.Conceptual Question Library: A library of 1000 Conceptual Questions available in the assignment manager requires students to apply their statistical understanding.

__Motivation__

EXPANDED! MyLab Statistics exercisesare newly mapped to improve student learning outcomes. Homework reinforces and supports students’ understanding of key statistics topics.UPDATED! Step-by-Step Example videosguide students through the process of analyzing a problem using the “Think, Show, and Tell” strategy from the textbook.

- Author in Action Videos feature author Paul Velleman teaching introductory statistics to undergraduate students at Cornell University.
- Simulation Applets use technology to help students learn and visualize a wide range of topics covered in introductory statistics.
StatTalk Videos: Hosted by fun-loving statistician Andrew Vickers, this video series demonstrates important statistical concepts through interesting stories and real-life events. Videos include assessment questions and an instructor’s guide.Learning Catalytics™, now available with MyLab Statistics, is a student response tool that uses students’ smartphones, tablets, or laptops to engage them in more interactive tasks and thinking. It helps to foster student engagement and peer-to-peer learning, generate class discussion, and guide lectures with real-time analytics. Now access pre-built exercises created by leading Pearson authors.

Pearson works continuously to ensure our products are as accessible as possible to all students. We are working toward achieving WCAG 2.0 Level AA and Section 508 standards, as expressed in the Pearson Guidelines for Accessible Educational Web Media.

__Reflects the new Guidelines for Assessment and Instruction in Statistics Education (GAISE) 2016 report adopted by the American Statistical Association to encourage statistical thinking__

**Random Matters:**This new feature encourages a gradual, cumulative understanding of randomization. The first Random Matters box introduces drawing inferences from data. Subsequent Random Matters features draw histograms of sample means, introduce the thinking involved in permutation tests, and encourage judgment about how likely the observed statistic seems when viewed against the simulated sampling distribution of the null hypothesis.**Streamlined coverage of descriptive statistics**helps students progress more quickly through the first part of the book. Also a GAISE recommendation, random variables and probability distributions are now covered later in the text to allow for more time on the more critical statistical concepts.**Technology is utilized to improve the learning of two of the most difficult concepts in the introductory course**: the idea of a sampling distribution and the reasoning of statistical inference.

__Supports learning through worked examples and practice opportunities__

**A third variable is introduced with contingency tables and mosaic plots in Chapter 3**to give students earlier experience with multivariable thinking. Then, following the discussion of correlation and regression as a tool (without inference) in Chapters 6, 7, and 8, multiple regression is introduced in Chapter 9.

**Expanded and revised**guide students through the process of analyzing a problem through worked examples. They illustrate the importance of thinking about a statistics question (Think) and reporting findings (Tell)). The Show step contains the mechanics of calculating results. This results in a better understanding of the concept and problem-solving process that goes beyond number crunching.*Think, Show, Tell examples*

**New Web tools**that provide interactive versions of the distribution tables at the back of the book and tools for randomization inference methods such as the bootstrap and for repeated sampling from larger populations can be found online at astools.datadesk.com.

**Also available with MyLab Statistics**

MyLab™ Statistics is the teaching and learning platform that empowers you to reach every student. By combining trusted author content with digital tools and a flexible platform, MyLab Statistics personalizes the learning experience and improves results for each student. With MyLab Statistics and StatCrunch, an integrated web-based statistical software program, students learn the skills they need to interact with data in the real world. Learn more about MyLab Statistics.

MyLab Statistics exercisesare newly mapped to improve student learning outcomes. Homework reinforces and supports students’ understanding of key statistics topics.Updated Step-by-Step Example videosguide students through the process of analyzing a problem using the “Think, Show, and Tell” strategy from the textbook.

- Author in Action Videos feature author Paul Velleman teaching introductory statistics to undergraduate students at Cornell University.
- Simulation Applets use technology to help students learn and visualize a wide range of topics covered in introductory statistics.
Learning Catalytics™, now available with MyLab Statistics, is a student response tool that uses students’ smartphones, tablets, or laptops to engage them in more interactive tasks and thinking. It helps to foster student engagement and peer-to-peer learning, generate class discussion, and guide lectures with real-time analytics. Now access pre-built exercises created by leading Pearson authors.

Pearson works continuously to ensure our products are as accessible as possible to all students. We are working toward achieving WCAG 2.0 Level AA and Section 508 standards, as expressed in the Pearson Guidelines for Accessible Educational Web Media.

PART I: EXPLORING AND UNDERSTANDING DATA

**1. Stats Starts here**

1.1 What Is Statistics?

1.2. Data

1.3 Variables

1.4 Models

**2. Displaying and Describing Data**

2.1 Summarizing and Displaying a Categorical Variable

2.2 Displaying a Quantitative variable

2.3 Shape

2.4 Center

2.5 Spread

**3. Relationships Between Categorical Variables — Contingency Tables**

3.1 Contingency tables

3.2 Conditional distributions

3.3 Displaying Contingency Tables

3.4 Three Categorical Variables

**4. Understanding and Comparing Distributions**

4.1 Displays for Comparing Groups

4.2 Outliers

4.3 Re-Expressing Data: A First Look

**5. The Standard Deviation as a Ruler and the Normal Model**

5.1 Using the standard deviation to Standardize Values

5.2 Shifting and scaling

5.3 Normal models

5.4 Working with Normal Percentiles

5.5 Normal Probability Plots

Part I Review

PART II: EXPLORING RELATIONSHIPS BETWEEN VARIABLES

**6. Scatterplots, Association, and Correlation**

6.1 Scatterplots

6.2 Correlation

6.3 Warning: Correlation ≠ Causation

6.4 *Straightening Scatterplots

**7. Linear Regression**

7.1 Least Squares: The Line of “Best Fit”

7.2 The Linear model

7.3 Finding the least squares line

7.4 Regression to the Mean

7.5 Examining the Residuals

7.6 R^{2}–The Variation Accounted for by the Model

7.7 Regression Assumptions and Conditions

**8. Regression Wisdom**

8.1 Examining Residuals

8.2 Extrapolation: Reaching Beyond the Data

8.3 Outliers, Leverage, and Influence

8.4 Lurking Variables and Causation

8.5 Working with Summary Values

8.6 * Straightening Scatterplots–The Three Goals

8.7 * Finding a Good Re-Expression

**9. Multiple Regression**

9.1 What Is Multiple Regression?

9.2 Interpreting Multiple Regression Coefficients

9.3 The Multiple Regression Model–Assumptions and Conditions

9.4 Partial Regression Plots

9.5 Indicator Variables

Part II Review

PART III: GATHERING DATA

**10. Sample Surveys**

10.1 The Three Big Ideas of Sampling

10.2 Populations and Parameters

10.3 Simple Random Samples

10.4 Other Sampling Designs

10.5 From the Population to the Sample: You Can’t Always Get What You Want

10.6 The valid survey

10.7 Common Sampling Mistakes, or How to Sample Badly

**11. Experiments and Observational Studies**

11.1 Observational Studies

11.2 Randomized, Comparative Experiments

11.3 The Four Principles of Experiment Design

11.4 Control Groups

11.5 Blocking

11.6 Confounding

Part III Review

PART IV INFERENCE FOR ONE PARAMETER

**12. From Randomness to Probability **

12.1 Random phenomena

12.2 Modeling Probability

12.3 Formal Probability

12.4. Conditional Probability and the General Multiplication Rule

12.5 Independence

12.6 Picturing Probability: Tables, Venn Diagrams, and Trees

12.7 *Reversing the Conditioning: Bayes’ Rule

**13. Sampling Distributions and Confidence Intervals for Proportions**

13.1 The Sampling Distribution for a Proportion

13.2 When Does the Normal Model Work? Assumptions and Conditions

13.3 A Confidence Interval for a Proportion

13.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?

13.5 Margin of Error: Certainty vs. Precision

13.6 *Choosing your Sample Size

**14. Confidence Intervals for Means**

14.1 The Central Limit Theorem

14.2 A Confidence interval for the Mean

14.3 Interpreting confidence intervals

14.4 *Picking our Interval up by our Bootstraps

14.5 Thoughts about Confidence Intervals

**15. Testing Hypotheses**

15.1 Hypotheses

15.2 P-values

15.3 The Reasoning of Hypothesis Testing

15.4 A Hypothesis Test for the Mean

15.5 Intervals and Tests

15.6 P-Values and Decisions: What to Tell About a Hypothesis Test

**16. More About Tests and Intervals**

16.1 Interpreting P-values

16.2 Alpha Levels and Critical Values

16.3 Practical vs Statistical Significance

16.4 Errors

Part IV Review

PART V: INFERENCE FOR RELATIONSHIPS

**17. Comparing Groups**

17.1 A Confidence Interval for the Difference Between Two Proportions

17.2 Assumptions and Conditions for Comparing Proportions

17.3 The Two-Sample z-Test: Testing the Difference Between Proportions

17.4 A Confidence Interval for the Difference Between Two Means

17.5 The Two-Sample t-Test: Testing for the Difference Between Two Means

17.6 Randomization-Based Tests and Confidence Intervals for Two Means

17.7 *Pooling

17.8 *The Standard Deviation of a Difference

**18. Paired Samples and Blocks**

18.1 Paired Data

18.2 Assumptions and Conditions

18.3 Confidence Intervals for Matched Pairs

18.4 Blocking

**19. Comparing Counts**

19.1 Goodness-of-Fit Tests

19.2 Chi-Square Tests of Homogeneity

19.3 Examining the Residuals

19.4 Chi-Square Test of Independence

**20. Inferences for Regression**

20.1 The Regression Model

20.2 Assumptions and Conditions

20.3 Regression Inference and Intuition

20.4 The Regression Table

20.5 Multiple Regression Inference

20.6 Confidence and Prediction Intervals

20.7 *Logistic Regression

Part V Review

* Indicates optional section

**Richard D. De Veaux **is an internationally known educator and consultant. He has taught at the Wharton School and the Princeton University School of Engineering, where he won a “Lifetime Award for Dedication and Excellence in Teaching.” He is the C. Carlisle and M. Tippit Professor of Statistics at Williams College, where he has taught since 1994. Dick has won both the Wilcoxon and Shewell awards from the American Society for Quality. He is a fellow of the American Statistical Association (ASA) and an elected member of the International Statistical Institute (ISI). In 2008, he was named Statistician of the Year by the Boston Chapter of the ASA. Dick is also well known in industry, where for more than 30 years he has consulted for such Fortune 500 companies as American Express, Hewlett-Packard, Alcoa, DuPont, Pillsbury, General Electric, and Chemical Bank. Because he consulted with Mickey Hart on his book *Planet Drum*, he has also sometimes been called the “Official Statistician for the Grateful Dead.” His real-world experiences and anecdotes illustrate many of this book’s chapters.

Dick holds degrees from Princeton University in Civil Engineering (B.S.E.) and Mathematics (A.B.) and from Stanford University in Dance Education (M.A.) and Statistics (Ph.D.), where he studied dance with Inga Weiss and Statistics with Persi Diaconis. His research focuses on the analysis of large data sets and data mining in science and industry.

In his spare time, he is an avid cyclist and swimmer. He also is the founder of the “Diminished Faculty,” an a cappella Doo-Wop quartet at Williams College, and sings bass in the college concert choir and with the Choeur Vittoria of Paris. Dick is the father of four children.

**Paul F. Velleman** has an international reputation for innovative Statistics education. He is the author and designer of the multimedia Statistics program *ActivStats*, for which he was awarded the EDUCOM Medal for innovative uses of computers in teaching statistics, and the ICTCM Award for Innovation in Using Technology in College Mathematics. He also developed the award-winning statistics program *Data Desk*, and the Internet site Data and Story Library (DASL) (ASL.datadesk.com), which provides data sets for teaching Statistics. Paul’s understanding of using and teaching with technology informs much of this book’s approach.

Paul has taught Statistics at Cornell University since 1975, where he was awarded the MacIntyre Award for Exemplary Teaching. He holds an A.B. from Dartmouth College in Mathematics and Social Science, and M.S. and Ph.D. degrees in Statistics from Princeton University, where he studied with John Tukey. His research often deals with statistical graphics and data analysis methods. Paul co-authored (with David Hoaglin) *ABCs of Exploratory Data Analysis*. Paul is a Fellow of the American Statistical Association and of the American Association for the Advancement of Science. Paul is the father of two boys.

**David E. Bock** taught mathematics at Ithaca High School for 35 years. He has taught Statistics at Ithaca High School, Tompkins-Cortland Community College, Ithaca College, and Cornell University. Dave has won numerous teaching awards, including the MAA’s Edyth May Sliffe Award for Distinguished High School Mathematics Teaching (twice), Cornell University’s Outstanding Educator Award (three times), and has been a finalist for New York State Teacher of the Year.

Dave holds degrees from the Universi

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