Loose-leaf Version for The Basic Practice of Statistics

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  • Edition: 7th
  • Format: Loose-leaf
  • Copyright: 1/9/2015
  • Publisher: W. H. Freeman

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David Moore’s data analysis (conceptual) approach, which revolutionized the introductory statistics textbook, moves students away from formulas and number-crunching, focusing instead on how working statisticians in a variety of fields collect and analyze data, and use the results to tackle real-world problems.

The clear, direct way of emphasizing the course’s relevance and confronting students’ math anxieties is at the heart of the bestselling The Basic Practice of Statistics (BPS). It is also the ideal approach for taking full advantage of the powerful statistical tools and interactive learning features in this new edition’s text/media package. Now more than ever, BPS is ready to help students move from reading about statistical practice to practicing statistics themselves.

Author Biography

David S. Moore is Shanti S. Gupta Distinguished Professor of Statistics, Emeritus, at Purdue University and was 1998 president of the American Statistical Association. He received his A.B. from Princeton and his Ph.D. from Cornell, both in mathematics. He has written many research papers in statistical theory and served on the editorial boards of several major journals. Professor Moore is an elected fellow of the American Statistical Association and of the Institute of Mathematical Statistics and an elected member of the International Statistical Institute. He has served as program director for statistics and probability at the National Science Foundation.  In recent years, Professor Moore has devoted his attention to the teaching of statistics. He was the content developer for the Annenberg/Corporation for Public Broadcasting college-level telecourse Against All Odds: Inside Statistics and for the series of video modules Statistics: Decisions through Data, intended to aid the teaching of statistics in schools. He is the author of influential articles on statistics education and of several leading texts. Professor Moore has served as president of the International Association for Statistical Education and has received the Mathematical Association of America’s national award for distinguished college or university teaching of mathematics.

William I. Notz is Professor of Statistics at the Ohio State University.  He received his B.S. in physics from the Johns Hopkins University and his Ph.D. in mathematics from Cornell University.  His first academic job was as an assistant professor in the Department of Statistics at Purdue University.  While there, he taught the introductory concepts course with Professor Moore and as a result of this experience he developed an interest in statistical education.  Professor Notz is a co-author of EESEE (the Electronic Encyclopedia of Statistical Examples and Exercises) and co-author of Statistics: Concepts and Controversies.

Professor Notz’s research interests have focused on experimental design and computer experiments.  He is the author of several research papers and of a book on the design and analysis of computer experiments.  He is an elected fellow of the American Statistical Association.  He has served as the editor of the journal Technometrics and as editor of the Journal of Statistics Education.  He has served as the Director of the Statistical Consulting Service, as acting chair of the Department of Statistics for a year, and as an Associate Dean in the College of Mathematical and Physical Sciences at the Ohio State University.  He is a winner of the Ohio State University’s Alumni Distinguished Teaching Award. 

Michael A. Fligner is an Adjunct Professor at the University of California at Santa Cruz and a non-resident Professor Emeritus with the Ohio State University. He received his B.S. in mathematics from the State University of New York at Stony Brook and his Ph.D. from the University of Connecticut. He spent almost 40 years at the Ohio State University where he was Vice Chair of the Department for over 10 years and also served as Director of the Statistical Consulting Service. He has done consulting work with several large corporations in Central Ohio. Professor Fligner's research interests are in Nonparametric Statistical methods and he received the Statistics in Chemistry award from the American Statistical Association for work on detecting biologically active compounds. He is co-author of the book Statistical Methods for Behavioral Ecology and received a Fulbright scholarship under the American Republics Research program to work at the Charles Darwin Research Station in the Galapagos Islands. He has been an Associate Editor of the Journal of Statistical Education. Professor Fligner is currently associated with the Center for Statistical Analysis in the Social Sciences at the University of California at Santa Cruz.

Table of Contents

0 Getting Started

Part I Exploring Data

1. Picturing Distributions with Graphs

2. Describing Distributions with Numbers

3. The Normal Distributions

4. Scatterplots and Correlation

5. Regression

6. Two-Way Tables*

7. Exploring Data: Part I Review

Part II Producing Data

8. Producing Data: Sampling

9. Producing Data: Experiments

10. Data Ethics*

11. Producing Data: Part II Review

Part III From Data Production to Inference

12. Introducing Probability

13. General Rules of Probability*

14. Binomial Distributions*

15. Sampling Distributions

16. Confidence Intervals: The Basics

17 Tests of Significance: The Basics

18. Inference in Practice

19. From Exploration to Inference: Part II Review

Part IV Inference About Variables

20. Inference about a Population Mean

21. Two-Sample Problems

22. Inference about a Population Proportion

23. Comparing Two Proportions

24. Inference about Variables: Part IV Review

Part V Inference About Relationships

25. Two Categorical Variables: The Chi-Square Test

26. Inference for Regression

27. One-Way Analysis of Variance: Comparing Several Means

Part VI Optional Companion Chapters (available online)

28. Nonparametric Tests

29. Statistical Process Control

30. Multiple Regression

31. More about Analysis of Variance

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