Were you looking for the book with access to MyStatLab? This product is the book alone and does NOT come with access to MyStatLab. Buy the book and access card package to save money on this resource. Richard De Veaux, Paul Velleman, and David Bock wrote Intro Stats 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,…mehr
Were you looking for the book with access to MyStatLab? This product is the book alone and does NOT come with access to MyStatLab. Buy the book and access card package to save money on this resource. Richard De Veaux, Paul Velleman, and David Bock wrote Intro Stats 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. New to the Fourth Edition is a streamlined presentation that keeps students focused on what's most important, while including out helpful features. An updated organization divides chapters into sections, with specific learning objectives to keep students on track. A detailed table of contents assists with navigation through this new layout. Single-concept exercises complement the existing mid- to hard-level exercises for basic skill development. This text is also available with MyStatLab™?please see the Features section or visit www.mystatlab.com for more information.
Dick De Veaux (Williams College) is an award-winning teacher and consultant to major corporations. His real-world experiences and anecdotes illustrate many of the chapters. Dick has taught business students at Wharton, engineering students at Princeton, and liberal arts students at Williams. Dick was named the 2008 Mosteller Statistician of the Year, awarded by the Boston chapter of the American Statistical Association for exceptional contributions to the field of statistics and outstanding service to the statistical community. To learn more, please go to: http://www.williams.edu/admin/news/releases/1624/. Paul Velleman (Cornell University) is the only statistician to win the EDUCAUSE award for innovating technology for learning. The developer of ActivStats® multimedia software, Data Desk® statistics software, and the DASL online archive of teaching datasets, his understanding of using and teaching with technology informs much of the book’s approach. David Bock (Cornell University) won awards as a high school teacher of AP calculus and statistics and was a grader for the AP Statistics program from its inception. He is now the chief extension officer for the Cornell University mathematics department in charge of outreach to K-12 teachers. Dave’s wisdom about how students learn helps to shape the book’s pedagogy.
Inhaltsangabe
Preface Index of Applications
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
Review of Part I: Exploring and Understanding Data
Part II. Exploring Relationships Between Variables
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
Review of Part II: Exploring Relationships Between Variables
Part III. Gathering Data
9. Understanding Randomness 9.1 What is Randomness? 9.2 Simulating By Hand
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 Experimental Design 11.4 Control Treatments 11.5 Blocking 11.6 Confounding
Review of Part III: Gathering Data
Part IV. Randomness and Probability
12. From Randomness to Probability 12.1 Random Phenomena 12.2 Modeling Probability 12.3 Formal Probability
13. Probability Rules! 13.1 The General Addition Rule 13.2 Conditional Probability and the General Multiplication Rule 13.3 Independence 13.4 Picturing Probability: Tables, Venn Diagrams and Trees 13.5 Reversing the Conditioning and Bayes' Rule
14. Random Variables and Probability Models 14.1 Expected Value: Center 14.2 Standard Deviation 14.3 Combining Random Variables 14.4 The Binomial Model 14.5 Modelin
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
Review of Part I: Exploring and Understanding Data
Part II. Exploring Relationships Between Variables
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
Review of Part II: Exploring Relationships Between Variables
Part III. Gathering Data
9. Understanding Randomness 9.1 What is Randomness? 9.2 Simulating By Hand
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 Experimental Design 11.4 Control Treatments 11.5 Blocking 11.6 Confounding
Review of Part III: Gathering Data
Part IV. Randomness and Probability
12. From Randomness to Probability 12.1 Random Phenomena 12.2 Modeling Probability 12.3 Formal Probability
13. Probability Rules! 13.1 The General Addition Rule 13.2 Conditional Probability and the General Multiplication Rule 13.3 Independence 13.4 Picturing Probability: Tables, Venn Diagrams and Trees 13.5 Reversing the Conditioning and Bayes' Rule
14. Random Variables and Probability Models 14.1 Expected Value: Center 14.2 Standard Deviation 14.3 Combining Random Variables 14.4 The Binomial Model 14.5 Modelin
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