I: EXPLORING AND UNDERSTANDING DATA 1. Stats Starts Here 1.1 What Is Statistics?1.2 Data1.3 Variables1.4 Models 2. Displaying and Describing Data 2.1 Summarizing and Displaying a Categorical Variable2.2 Displaying a Quantitative Variable2.3 Shape2.4 Center2.5 Spread 3. Relationships Between Categorical Variables–Contingency Tables 3.1 Contingency Tables3.2 Conditional Distributions3.3 Displaying Contingency Tables3.4 Three Categorical Variables 4. Understanding and Comparing Distributions 4.1 Displays for Comparing Groups4.2 Outliers4.3 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 Values5.2 Shifting and Scaling5.3 Normal Models5.4 Working with Normal Percentiles5.5 Normal Probability PlotsReview of Part I: Exploring and Understanding Data II. EXPLORING RELATIONSHIPS BETWEEN VARIABLES 6. Scatterplots, Association, and Correlation 6.1 Scatterplots6.2 Correlation6.3 Warning: Correlation ≠ Causation6.4 Straightening Scatterplots 7. Linear Regression 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. Regression Wisdom 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. Multiple Regression 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 VariablesReview of Part II: Exploring Relationships Between Variables 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. Experiments and Observational Studies 11.1 Observational Studies11.2 Randomized, Comparative Experiments11.3 The Four Principles of Experimental Design11.4 Control Groups11.5 Blocking11.6 ConfoundingReview of Part III: Gathering Data IV. RANDOMNESS AND PROBABILITY 12. From Randomness to Probability 12.1 Random Phenomena12.2 Modeling Probability12.3 Formal Probability 13. Probability Rules! 13.1 The General Addition Rule13.2 Conditional Probability and the General Multiplication Rule13.3 Independence13.4 Picturing Probability: Tables, Venn Diagrams, and Trees13.5 Reversing the Conditioning and Bayes' Rule 14. Random Variables 14.1 Center: The Expected Value14.2 Spread: The Standard Deviation14.3 Shifting and Combining Random Variables14.4 Continuous Random Variables 15. Probability Models 15.1 Bernoulli Trials15.2 The Geometric Model15.3 The Binomial Model15.4 Approximating the Binomial with a Normal Model15.5 The Continuity Correction15.6 The Poisson Model15.7 Other Continuous Random Variables: The Uniform and the ExponentialReview of Part IV: Randomness and Probability V. INFERENCE FOR ONE PARAMETER 16. Sampling Distribution Models and Confidence Intervals for Proportions 16.1 The Sampling Distribution Model for a Proportion16.2 When Does the Normal Model Work? Assumptions and Conditions16.3 A Confidence Interval for a Proportion16.4 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean?16.5 Margin of Error: Certainty vs. Precision16.6 Choosing the Sample Size 17. Confidence Intervals for Means 17.1 The Central Limit Theorem17.2 A Confidence Interval for the Mean17.3 Interpreting Confidence Intervals17.4 Picking Our Interval up by Our Bootstraps17.5 Thoughts About Confidence Intervals 18. Testing Hypotheses 18.1 Hypotheses18.2 P-Values18.3 The Reasoning of Hypothesis Testing18.4 A Hypothesis Test for the Mean18.5 Intervals and Tests18.6 P-Values and Decisions: What to Tell About a Hypothesis Test 19. More About Tests and Intervals 19.1 Interpreting P-Values19.2 Alpha Levels and Critical Values19.3 Practical vs. Statistical Significance19.4 ErrorsReview of Part V: Inference for One Parameter VI. INFERENCE FOR RELATIONSHIPS 20. Comparing Groups 20.1 A Confidence Interval for the Difference Between Two Proportions20.2 Assumptions and Conditions for Comparing Proportions20.3 The Two-Sample z-Test: Testing for the Difference Between Proportions20.4 A Confidence Interval for the Difference Between Two Means20.5 The Two-Sample t-Test: Testing for the Difference Between Two Means20.6 Randomization Tests and Confidence Intervals for Two Means20.7 Pooling20.8 The Standard Deviation of a Difference 21. Paired Samples and Blocks 21.1 Paired Data21.2 The Paired t-Test21.3 Confidence Intervals for Matched Pairs21.4 Blocking 22. Comparing Counts 22.1 Goodness-of-Fit Tests22.2 Chi-Square Test of Homogeneity22.3 Examining the Residuals22.4 Chi-Square Test of Independence 23. Inferences for Regression 23.1 The Regression Model23.2 Assumptions and Conditions23.3 Regression Inference and Intuition23.4 The Regression Table23.5 Multiple Regression Inference23.6 Confidence and Prediction Intervals23.7 Logistic Regression23.8 More About RegressionReview of Part VI: Inference for Relationships VII. INFERENCE WHEN VARIABLES ARE RELATED 24. Multiple Regression Wisdom 24.1 Multiple Regression Inference24.2 Comparing Multiple Regression Model24.3 Indicators24.4 Diagnosing Regression Models: Looking at the Cases24.5 Building Multiple Regression Models 25. Analysis of Variance 25.1 Testing Whether the Means of Several Groups Are Equal25.2 The ANOVA Table25.3 Assumptions and Conditions25.4 Comparing Means25.5 ANOVA on Observational Data 26. Multifactor Analysis of Variance 26.1 A Two Factor ANOVA Model26.2 Assumptions and Conditions26.3 Interactions 27. Statistics and Data Science 27.1 Introduction to Data MiningReview of Part VII: Inference When Variables Are Related Parts I - V Cumulative Review Exercises Appendices Answers Credits Indexes Tables and Selected Formulas
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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 Think/Show/Tell Step-by-Step Examples 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. 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 summarize the story told by the chapter and provide 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. 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. New - Web tools 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. Check out the preface for a complete list of features and what's new in this edition. 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 This is one of the biggest challenges in statistics courses. Pearson offers a variety of content and course options to support students with just-in-time remediation and key-concept review as needed. 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 Successful students have the ability to apply their statistical ideas and knowledge to new concepts and real-world situations. Providing frequent opportunities for data analysis and interpretation helps students develop the 21st-century skills that they need to be successful in the classroom and workplace. 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.New - StatCrunch Projects in MyLab Statistics provide opportunities for students to explore data beyond the classroom. In each project, students analyze a large data set in StatCrunch and answer corresponding, assignable questions for immediate feedback. StatCrunch Projects span the entire curriculum or focus on certain key concepts. Questions from each project can also be assigned individually.Technology-Specific Video Tutorials address how to use different technologies to complete exercises.Technology-Specific Study Cards provide 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 homework exercises and from the textbook can be analyzed directly in StatCrunch or uploaded to other  statistical software.Conceptual Question Library: A library of 1000 conceptual questions in the Assignment Manager requires students to apply their statistical understanding. Motivation Students are motivated to succeed when they're engaged in the learning experience and understand the relevance and power of statistics. Through online homework, students receive immediate feedback and tutorial assistance that motivates them to do more, which means they retain more knowledge, improve their test scores, and perform better in future courses. Plus, we're always adding new solutions to further engage students. Expanded - MyLab Statistics exercises are newly mapped to improve student learning outcomes. Homework reinforces and supports students’ understanding of key statistics topics.Updated - Step-by-Step Example videos guide 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.
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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 Think/Show/Tell Step-by-Step Examples 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. 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 StatCrunch Projects in MyLab Statistics provide opportunities for students to explore data beyond the classroom. In each project, students analyze a large data set in StatCrunch and answer corresponding, assignable questions for immediate feedback. StatCrunch Projects span the entire curriculum or focus on certain key concepts. Questions from each project can also be assigned individually.MyLab Statistics exercises are newly mapped to improve student learning outcomes. Homework reinforces and supports students’ understanding of key statistics topics.Updated Think/Show/Tell Step-by-Step Example videos guide 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. Check out the preface for a complete list of features and what's new in this edition.
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Produktdetaljer

ISBN
9780135163979
Publisert
2019-09-04
Utgave
5. utgave
Utgiver
Vendor
Pearson
Aldersnivå
U, 05
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
304

Biographical note

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.