Intro Stats, Books a la carte Plus NEW MyStatLab with Pearson eText -- Access Card Package / Edition 4 available in Other Format
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For courses in Introductory Statistics.
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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.
Personalize learning with MyLab Statistics
Used by nearly one million students a year, MyLab™ Statistics is the world’s leading online program for teaching and learning statistics. MyLab Statistics delivers assessment, tutorials, and multimedia resources that provide engaging and personalized experiences for each student, so learning can happen in any environment. Each course is developed to accompany Pearson’s best-selling content, authored by thought leaders across the statistics curriculum, and can be easily customized to fit any course format.
0134210247 / 9780134210247 Intro Stats, Books a la Carte Plus NEW MyLab Statistics with Pearson eText - Access Card Package, 5/e
Package consists of:
- 013421028X / 9780134210285 Intro Stats, Books a la Carte Edition, 5/e
- 0134768361 / 9780134768366 MyLab Statistics with Pearson eText - Standalone Access Card - for Intro Stats 5/e
|Product dimensions:||8.40(w) x 10.90(h) x 1.10(d)|
About the Author
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.
Table of Contents
PART I: EXPLORING AND UNDERSTANDING DATA
1. Stats Starts here
1.1 What Is Statistics?
2. Displaying and Describing Data
2.1 Summarizing and Displaying a Categorical Variable
2.2 Displaying a Quantitative variable
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
4.3 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
6.3 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 R2–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
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
12.6 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
15.3 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
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
17.8 *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
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