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Note: Supplemental materials are not guaranteed with Rental or Used book purchases.
Purchase Benefits
7-Day eTextbook Access
What is included with this book?
For courses in Introductory Statistics.
Encourages statistical thinking using technology, innovative methods, and a sense of humor
Inspired by the 2016 GAISE Report revision, Intro Stats, 5th Edition by De Veaux/Velleman/Bock uses innovative strategies to help students think critically about data – while maintaining the book’s core concepts, coverage, and most importantly, readability.
By using technology and simulations to demonstrate variability at critical points throughout the course, the authors make it easier for students to understand more complicated statistical later in the course (such as the Central Limit Theorem). In addition, students get more exposure to large data sets and multivariate thinking, which better prepares them to be critical consumers of statistics in the 21st century.
The 5th Edition’s approach to teaching intro stats is revolutionary, while retaining its lively tone and popular features such as Think/Show/Tell examples.
Also available with MyLab Statistics
MyLab™ Statistics is the teaching and learning platform that empowers instructors 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.
Note: You are purchasing a standalone product; MyLab Statistics does not come packaged with this content. Students, if interested in purchasing this title with MyLab Statistics, ask your instructor to confirm the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information.
If you would like to purchase both the physical text and MyLab Statistics, search for:
0134210239 / 9780134210230 Intro Stats Plus NEW MyLab Statistics with Pearson eText - Access Card Package, 5/e
Package consists of:
0134210220 / 9780134210223 Intro Stats, 5/e
0134768361 / 9780134768366 MyLab Statistics with Pearson eText - Standalone Access Card - for Intro Stats 5/e
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 University at Albany in Mathematics (B.A.) and Statistics/Education (M.S.). Dave has been a reader and table leader for the AP Statistics exam, serves as a Statistics consultant to the College Board, and leads workshops and institutes for AP Statistics teachers. He has served as K—12 Education and Outreach Coordinator and a senior lecturer for the Mathematics Department at Cornell University. His understanding of how students learn informs much of this book’s approach.
Dave and his wife relax by biking or hiking, spending much of their free time in Canada, the Rockies, or the Blue Ridge Mountains. They have a son, a daughter, and four grandchildren.
PART I: EXPLORING AND UNDERSTANDING DATA
1. Stats Starts here
1.1 What Is Statistics?1.2. Data1.3 Variables1.4 Models
2.1 Summarizing and Displaying a Categorical Variable2.2 Displaying a Quantitative variable2.3 Shape2.4 Center2.5 Spread
3.1 Contingency tables3.2 Conditional distributions3.3 Displaying Contingency Tables3.4 Three Categorical Variables
4.1 Displays for Comparing Groups4.2 Outliers4.3 Re-Expressing Data: A First Look
5.1 Using the standard deviation to Standardize Values5.2 Shifting and scaling5.3 Normal models5.4 Working with Normal Percentiles5.5 Normal Probability Plots
Part I Review
PART II: EXPLORING RELATIONSHIPS BETWEEN VARIABLES
6. Scatterplots, Association, and Correlation
6.1 Scatterplots6.2 Correlation6.3 Warning: Correlation ≠ Causation6.4 *Straightening Scatterplots
7.1 Least Squares: The Line of “Best Fit”7.2 The Linear model7.3 Finding the least squares line7.4 Regression to the Mean7.5 Examining the Residuals7.6 R2–The Variation Accounted for by the Model7.7 Regression Assumptions and Conditions
8.1 Examining Residuals8.2 Extrapolation: Reaching Beyond the Data8.3 Outliers, Leverage, and Influence8.4 Lurking Variables and Causation8.5 Working with Summary Values8.6 * Straightening Scatterplots–The Three Goals8.7 * Finding a Good Re-Expression
9.1 What Is Multiple Regression?9.2 Interpreting Multiple Regression Coefficients9.3 The Multiple Regression Model–Assumptions and Conditions9.4 Partial Regression Plots9.5 Indicator Variables
Part II Review
PART III: GATHERING DATA
10. Sample Surveys
10.1 The Three Big Ideas of Sampling10.2 Populations and Parameters10.3 Simple Random Samples10.4 Other Sampling Designs10.5 From the Population to the Sample: You Can’t Always Get What You Want10.6 The valid survey10.7 Common Sampling Mistakes, or How to Sample Badly
11.1 Observational Studies11.2 Randomized, Comparative Experiments11.3 The Four Principles of Experiment Design11.4 Control Groups11.5 Blocking11.6 Confounding
Part III Review
PART IV INFERENCE FOR ONE PARAMETER
12. From Randomness to Probability
12.1 Random phenomena12.2 Modeling Probability12.3 Formal Probability12.4. Conditional Probability and the General Multiplication Rule12.5 Independence12.6 Picturing Probability: Tables, Venn Diagrams, and Trees12.7 *Reversing the Conditioning: Bayes’ Rule
13.1 The Sampling Distribution for a Proportion13.2 When Does the Normal Model Work? Assumptions and Conditions13.3 A Confidence Interval for a Proportion13.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?13.5 Margin of Error: Certainty vs. Precision 13.6 *Choosing your Sample Size
14.1 The Central Limit Theorem14.2 A Confidence interval for the Mean14.3 Interpreting confidence intervals14.4 *Picking our Interval up by our Bootstraps14.5 Thoughts about Confidence Intervals
15.1 Hypotheses15.2 P-values15.3 The Reasoning of Hypothesis Testing15.4 A Hypothesis Test for the Mean15.5 Intervals and Tests15.6 P-Values and Decisions: What to Tell About a Hypothesis Test
16.1 Interpreting P-values16.2 Alpha Levels and Critical Values16.3 Practical vs Statistical Significance16.4 Errors
Part IV Review
PART V: INFERENCE FOR RELATIONSHIPS
17. Comparing Groups
17.1 A Confidence Interval for the Difference Between Two Proportions17.2 Assumptions and Conditions for Comparing Proportions17.3 The Two-Sample z-Test: Testing the Difference Between Proportions17.4 A Confidence Interval for the Difference Between Two Means17.5 The Two-Sample t-Test: Testing for the Difference Between Two Means17.6 Randomization-Based Tests and Confidence Intervals for Two Means17.7 *Pooling17.8 *The Standard Deviation of a Difference
18.1 Paired Data18.2 Assumptions and Conditions18.3 Confidence Intervals for Matched Pairs18.4 Blocking
19.1 Goodness-of-Fit Tests19.2 Chi-Square Tests of Homogeneity19.3 Examining the Residuals19.4 Chi-Square Test of Independence
20.1 The Regression Model20.2 Assumptions and Conditions20.3 Regression Inference and Intuition20.4 The Regression Table 20.5 Multiple Regression Inference20.6 Confidence and Prediction Intervals20.7 *Logistic Regression
Part V Review
* Indicates optional section
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