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Book

Stats: Data and Models, Global Edition (4e)

By Richard De Veaux, Paul Velleman, David E. Bock
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ISBN
9781292101637
Published date
08/06/2015
 
 
 

Description

Richard De Veaux, Paul Velleman, and David Bock wrote Stats: Data and Models with the goal that students and instructors have as much fun reading it as they did writing it. Maintaining a conversational, humorous, and informal writing style, this new edition engages students from the first page. The authors focus on statistical thinking throughout the text and rely on technology for calculations. As a result, students can focus on developing their conceptual understanding. Innovative Think/Show/Tell examples give students a problem-solving framework and, more importantly, a way to think through any statistics problem and present their results. The Fourth Edition is updated with instructor podcasts, video lectures, and new examples to keep material fresh, current, and relevant to today’s students.

Product details
ISBN
 
9781292101637
Edition
 
4th
Published date
 
08/06/2015
Published by
 
Pearson Higher Ed USA
Pages
 
996
Format
 
Table of contents
  • Preface
  • Part I: Exploring and Understanding Data
  • 1. Stats Starts Here
  • 1.1 What Is Statistics?
  • 1.2 Data
  • 1.3 Variables
  • 2. Displaying and Describing Categorical Data
  • 2.1 Summarizing and Displaying a Single Categorical variable
  • 2.2 Exploring the Relationship Between Two Categorical variables
  • 3. Displaying and Summarizing Quantitative Data  
  • 3.1 Displaying quantitative variables  
  • 3.2 Shape  
  • 3.3 Center
  • 3.4 Spread
  • 3.5 Boxplots and 5-Number Summaries
  • 3.6 The Center of Symmetric Distributions: The Mean
  • 3.7 The Spread of Symmetric Distributions: The Standard Deviation
  • 3.8 Summary—What to Tell About a quantitative variable
  • 4. Understanding and Comparing Distributions
  • 4.1 Comparing Groups with Histograms
  • 4.2 Comparing Groups with Boxplots
  • 4.3 Outliers
  • 4.4 Timeplots: Order, Please!
  • 4.5 Re-Expressing Data: A First Look
  • 5. The Standard Deviation as a Ruler and the Normal Model
  • 5.1 Standardizing with z-Scores
  • 5.2 Shifting and Scaling
  • 5.3 Normal Models
  • 5.4 Finding Normal Percentiles
  • 5.5 Normal Probability Plots
  • 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 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
  • 9. Re-expressing Data: Get It Straight!
  • 9.1 Straightening Scatterplots – The Four Goals
  • 9.2 Finding a Good Re-Expression
  • Part III: Gathering Data
  • 10. Understanding Randomness
  • 10.1 What Is Randomness?
  • 10.2 Simulating by Hand
  • 11. Sample Surveys
  • 11.1 The Three Big Ideas of Sampling
  • 11.2 Populations and Parameters
  • 11.3 Simple Random Samples
  • 11.4 Other Sampling Designs
  • 11.5 From the Population to the Sample: You Can’t Always Get What You Want
  • 11.6 The valid Survey
  • 11.7 Common Sampling Mistakes, or How to Sample Badly
  • 12. Experiments and Observational Studies
  • 12.1 Observational Studies
  • 12.2 Randomized, Comparative Experiments
  • 12.3 The Four Principles of Experimental Design
  • 12.4 Control Treatments
  • 12.5 Blocking
  • 12.6 Confounding
  • Part IV: Randomness and Probability
  • 13. From Randomness to Probability
  • 13.1 Random Phenomena
  • 13.2 Modeling Probability
  • 13.3 Formal Probability
  • 14. Probability Rules!
  • 14.1 The General Addition Rule
  • 14.2 Conditional Probability and the General Multiplication Rule
  • 14.3 Independence
  • 14.4 Picturing Probability: Tables, Venn Diagrams, and Trees
  • 14.5 Reversing the Conditioning and Bayes’ Rule
  • 15. Random Variables
  • 15.1 Center: The Expected value
  • 15.2 Spread: The Standard Deviation
  • 15.3 Shifting and Combining Random variables
  • 15.4 Continuous Random variables
  • 16. Probability Models
  • 16.1 Bernoulli Trials
  • 16.2 The Geometric Model
  • 16.3 The Binomial Model
  • 16.4 Approximating the Binomial with a Normal Model
  • 16.5 The Continuity Correction
  • 16.6 The Poisson Model
  • 16.7 Other Continuous Random Variables: The Uniform and the Exponential
  • Part V: From the Data at Hand to the World at Large
  • 17. Sampling Distribution Models
  • 17.1 Sampling Distribution of a Proportion
  • 17.2 When Does the Normal Model Work? Assumptions and Conditions
  • 17.3 The Sampling Distribution of Other Statistics
  • 17.4 The Central Limit Theorem: The Fundamental Theorem of Statistics  
  • 17.5 Sampling Distributions: A Summary
  • 18. Confidence Intervals for Proportions
  • 18.1 A Confidence Interval
  • 18.2 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?
  • 18.3 Margin of Error: Certainty vs. Precision  
  • 18.4 Assumptions and Conditions
  • 19. Testing Hypotheses About Proportions
  • 19.1 Hypotheses
  • 19.2 P-values
  • 19.3 The Reasoning of Hypothesis Testing
  • 19.4 Alternative Alternatives
  • 19.5 P-values and Decisions: What to Tell About a Hypothesis Test
  • 20. Inferences About Means
  • 20.1 Getting Started: The Central Limit Theorem (Again)
  • 20.2 Gosset’s t
  • 20.3 Interpreting Confidence Intervals
  • 20.4 A Hypothesis Test for the Mean
  • 20.5 Choosing the Sample Size
  • 21. More About Tests and Intervals
  • 21.1 Choosing Hypotheses  
  • 21.2 How to Think About P-values
  • 21.3 Alpha Levels  
  • 21.4 Critical values for Hypothesis Tests
  • 21.5 Errors
  • Part VI: Accessing Associations Between Variables
  • 22. Comparing Groups
  • 22.1 The Standard Deviation of a Difference
  • 22.2 Assumptions and Conditions for Comparing Proportions
  • 22.3 A Confidence Interval for the Difference Between Two Proportions
  • 22.4 The Two Sample z-Test: Testing for the Difference Between Proportions
  • 22.5 A Confidence Interval for the Difference Between Two Means  
  • 22.6 The Two-Sample t-Test: Testing for the Difference Between Two Means
  • 22.7 The Pooled t-Test: Everyone into the Pool?
  • 23. Paired Samples and Blocks
  • 23.1 Paired Data
  • 23.2 Assumptions and Conditions
  • 23.3 Confidence Intervals for Matched Pairs
  • 23.4 Blocking
  • 24. Comparing Counts
  • 24.1 Goodness-of-Fit Tests
  • 24.2 Chi-Square Test of Homogeneity
  • 24.3 Examining the Residuals
  • 24.4 Chi-Square Test of Independence
  • 25. Inferences for Regression
  • 25.1 The Population and the Sample
  • 25.2 Assumptions and Conditions
  • 25.3 Intuition About Regression Inference
  • 25.4 Regression Inference
  • 25.5 Standard Errors for Predicted values
  • 25.6 Confidence Intervals for Predicted values
  • 25.7 Logistic Regression
  • Part VII: Inference When Variables Are Related
  • 26. Analysis of Variance
  • 26.1 Testing Whether the Means of Several Groups Are Equal  
  • 26.2 The ANOVA Table
  • 26.3 Assumptions and Conditions
  • 26.4 Comparing Means
  • 26.5 ANOVA on Observational Data
  • 27. Multifactor Analysis of Variance
  • 27.1 A Two Factor ANOVA Model  
  • 27.2 Assumptions and Conditions
  • 27.3 Interactions
  • 28. Multiple Regression
  • 28.1 What Is Multiple Regression?
  • 28.2 Interpreting Multiple Regression Coefficients
  • 28.3 The Multiple Regression Model—Assumptions and Conditions
  • 28.4 Multiple Regression Inference
  • 28.5 Comparing Multiple Regression Models
  • 29. Multiple Regression Wisdom
  • 29.1 Indicators
  • 29.2 Diagnosing Regression Models: Looking at the Cases
  • 29.3 Building Multiple Regression Models
  • 29.4 Building Multiple Regression Models Sequentially
  • Appendixes
  • A: Answers
  • B: Photo Acknowledgments
  • C: Index
  • D: Tables and Selected Formulas
New to this edition

Exercise sets have been expanded with hundreds of new exercises and now feature an improved arrangement. They progress in difficulty from basic questions to complex, multi-step exercises that ask the student to synthesize and incorporate the ideas they’ve learned from previous chapters. Answers are provided for odd-numbered exercises.

  • Think/Show/Tell examples have been updated with new applications and data. These examples have been reworked so that each example begins with a clear question, and ends with a conclusion that answers the stated question.
  • Current data ensures that this book remains relevant to students and their interests. Most of the data used in examples and exercises are real and have been updated for the new edition. Data come from author experience, news stories and recent research articles.
  • For Example illustrative examples embedded throughout the chapter show how to apply concepts and methods discussed in the text. With about four new examples per chapter, there are more than 100 new examples in this edition.
  • Instructor Podcasts from the authors focus on the key points of each chapter, helping both new and experienced instructors prepare for class. These are available for download from MyStatLab.
  • Video Lectures were scripted and presented by the authors themselves, helping students review the important points in each chapter. Different video presenters also work through examples from the text.

 

MyStatLab not included. Students, if MyStatLab is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN and course ID. MyStatLab should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information.

Features & benefits

Data Analysis and Problem Solving

  • NEW! Exercise sets have been expanded with hundreds of new exercises and now feature an improved arrangement. They progress in difficulty from basic questions to complex, multi-step exercises that ask the student to synthesise and incorporate the ideas they’ve learned from previous chapters. Answers are provided for odd-numbered exercises.
  • Emphasis on data analysis encourages the use of technology to analyse data, so students focus on asking the right questions, critically analysing results, and drawing appropriate conclusions. Instructions are provided for major statistical packages.
  • The Think, Show, Tell approach to problem solving teaches students how to think statistically, show proper application of techniques, and tell others what they have learned. These step-by-step examples guide students through the problem with both a general explanation alongside the worked-out solution.
  • NEW! Think/Show/Tell examples have been updated with new applications and data. These examples have been reworked so that each example begins with a clear question, and ends with a conclusion that answers the stated question.
  • Math Boxes provide proofs, derivations, and formulas so that students can refer to the underlying mathematics for enhanced understanding.
  • The authors consistently discuss the Assumptions and Conditions necessary to perform a particular test, make a certain calculation, or arrive at an interpretation or conclusion in the worked examples and exercises.
  • NEW! Current data ensures that this book remains relevant to students and their interests. Most of the data used in examples and exercises are real and have been updated for the new edition. Data come from author experience, news stories and recent research articles. Whenever possible, the data are on the DVD so students can explore them further.

Real-Life Problems and Solutions

  • Where Are We Going? chapter openers begin each chapter with a real-life example. This feature demonstrates how the material fits in with what students already learned and prepares them for upcoming statistical concepts.
  • What Can Go Wrong? discussions in each chapter address common misuses and misunderstandings of statistics to arm students with the tools to detect statistical errors and debunk misuses of statistics.
  • What Have We Learned? summaries highlight concepts, terms, and skills that the student has learned in the chapter.
  • Just Checking questions in each chapter ask students to pause and think about what they’ve read to ensure that they understand the material presented thus far. Answers are at the end of the chapter.
  • NEW! For Example illustrative examples embedded throughout the chapter show how to apply concepts and methods discussed in the text. With about four new examples per chapter, there are more than 100 new examples in this edition.

Pointers, Notes, and Lectures from the Authors

  • ActivStats Pointers throughout the text indicate where ActivStats activities complement and enhance the discussions presented in the book.
  • Marginal Notation Alerts are included throughout the text to explain how to properly use the related statistical notation.
  • NEW! Instructor Podcasts from the authors focus on the key points of each chapter, helping both new and experienced instructors prepare for class. These are available for download from MyLab Statistics.
  • NEW! Video Lectures were scripted and presented by the authors themselves, helping students review the important points in each chapter. Different video presenters also work through examples from the text.