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The Basic Practice of Statistics
Eighth Edition| ©2018New Edition Available David S. Moore; William I. Notz; Michael Fligner
Written by an author team of accomplished leaders in statistics education, The Basic Practice of Statistics (BPS) reflects the actual practice of statistics, where data analysis and design of data production join with probability-based inference to form a coherent science of data. The auth...
Written by an author team of accomplished leaders in statistics education, The Basic Practice of Statistics (BPS) reflects the actual practice of statistics, where data analysis and design of data production join with probability-based inference to form a coherent science of data. The authors’ ultimate goal is to equip students to carry out common statistical procedures and to follow statistical reasoning in their fields of study and in their future employment.
The text’s long-standing renown is built on an inspired framework of balanced content, experience with data, and the importance of ideas. These themes are widely accepted by statisticians concerned about teaching and are directly connected to and reflected by the themes of the College Report of the Guidelines in Assessment and Instruction for Statistics Education (GAISE) Project.
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A defining text in statistics education, The Basic Practice of Statistics puts data analysis at the forefront and begins to develop students’ reasoning and judgment about statistical studies.
Written by an author team of accomplished leaders in statistics education, The Basic Practice of Statistics (BPS) reflects the actual practice of statistics, where data analysis and design of data production join with probability-based inference to form a coherent science of data. The authors’ ultimate goal is to equip students to carry out common statistical procedures and to follow statistical reasoning in their fields of study and in their future employment.
The text’s long-standing renown is built on an inspired framework of balanced content, experience with data, and the importance of ideas. These themes are widely accepted by statisticians concerned about teaching and are directly connected to and reflected by the themes of the College Report of the Guidelines in Assessment and Instruction for Statistics Education (GAISE) Project.
The eighth edition of The Basic Practice of Statistics is supported in SaplingPlus for a user experience of its own. SaplingPlus combines Macmillan’s StatTools, powerful multimedia resources, and text-specific exercises with the powerful targeted feedback of Sapling Learning, where every problem is a teaching and learning opportunity.
Features
The content, coverage, and features of The Basic Practice of Statistics are closely aligned to these GAISE recommendations.
4-Step Examples. Students learn how to use the four-step process for working through statistical problems: State, Plan, Solve, Conclude.
Apply Your Knowledge exercises. Major concepts are immediately reinforced with problems that are interspersed throughout the chapter.
Using Technology sections. Displays and comments on the output from graphing calculators, spreadsheets, and statistical software are located where appropriate.
Statistics in Your World highlights. These commentaries illustrate major concepts or present cautionary tales. Chapter Summary and Link It sections. Each chapter concludes with a summary of the chapter specifics, including major terms and processes, followed by a brief discussion of how the chapter links to material from both previous and upcoming chapters.
Check Your Skills and Chapter Exercises. Each chapter ends with a series of multiple-choice problems that test students’ understanding of basic concepts; a set of more in-depth exercises enable students to make judgments and draw conclusions based on real data and real scenarios.
New to This Edition
New Data Sets
New and updated examples and exercises, approximately 30% throughout the text, ensure that the content remains timely and relevant—and real!
Contents changes and reorganization
Based on the valuable feedback of instructors using The Basic Practice of Statistics as well as reviewers’ comments, the following changes enhance how statistics is taught today.
- Chapter 5. Coverage of big data added to the end of chapter 5 as an optional section
- Chapter 13. The order of topics in Chapter 13 has been rearranged. Conditional probability is now introduced using two-way tables of counts and the Bayes theorem is included as an optional section.
- Chapter 20. Section 20.8 “Resampling and standard errors” removed. (All resampling material now in online Chapter 32.)
- Chapter 21. Section 21.9 “Permutation tests” removed. (All resampling material now in online Chapter 32.)
- Chapters 27 and 30. Coverage of multiple comparisons will be removed from the online Chapter 30 “More About Analysis of Variance” and will be integrated into the print text; Chapter 27 “One-way ANOVA.” Chapter 30 will now cover only two-way ANOVA.
- Chapter 32 “Resampling: Permutation Tests and the Bootstrap”; “new” online chapter written to address courses that cover resampling.
Online technology appendices
The software basics technology appendices have been expanded to include more software options and moved online for convenient access www.macmillanhighered.com/bps8e.

The Basic Practice of Statistics
Eighth Edition| ©2018
David S. Moore; William I. Notz; Michael Fligner
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The Basic Practice of Statistics
Eighth Edition| 2018
David S. Moore; William I. Notz; Michael Fligner
Table of Contents
PART I: EXPLORING DATA
Chapter 0 Getting Started
Where data comes from matters
Always look at the data
Variation is everywhere
What lies ahead in this book
Chapter 1 Picturing Distributions with Graphs
1.1 Individuals and variables
1.2 Categorical variables: Pie charts and bar graphs
1.3 Quantitative variables: Histograms
1.4 Interpreting histograms
1.5 Quantitative variables: Stemplots
1.6 Time plots
Chapter 2 Describing Distributions with Numbers
2.1 Measuring center: The mean
2.2 Measuring center: The median
2.3 Comparing the mean and the median
2.4 Measuring spread: The quartiles
2.5 The five-number summary and boxplots
2.6 Spotting suspected outliers*
2.7 Measuring spread: The standard deviation
2.8 Choosing measures of center and spread
2.9 Using technology
2.10 Organizing a statistical problem
Chapter 3 The Normal Distributions
3.1 Density curves
3.2 Describing density curves
3.3 Normal distributions
3.4 The 68-95-99.7 rule
3.5 The standard Normal distribution
3.6 Finding Normal proportions
3.7 Using the standard Normal table
3.8 Finding a value given a proportion
Chapter 4 Scatterplots and Correlation
4.1 Explanatory and response variables
4.2 Displaying relationships: Scatterplots
4.3 Interpreting scatterplots
4.4 Adding categorical variables to scatterplots
4.5 Measuring linear association: Correlation
4.6 Facts about correlation
Chapter 5 Regression
5.1 Regression lines
5.2 The least-squares regression line
5.3 Using technology
5.4 Facts about least-squares regression
5.5 Residuals
5.6 Influential observations
5.7 Cautions about correlation and regression
5.8 Association does not imply causation
5.9 Correlation, prediction, and big data*
Chapter 6 Two-Way Tables*
6.1 Marginal distributions
6.2 Conditional distributions
6.3 Simpson's paradox
Chapter 7 Exploring Data: Part I Review
Part I Summary
Test Yourself
Supplementary Exercises
PART II: PRODUCING DATA
Chapter 8 Producing Data: Sampling
8.1 Population versus sample
8.2 How to sample badly
8.3 Simple random samples
8.4 Inference about the population
8.5 Other sampling designs
8.6 Cautions about sample surveys
8.7 The impact of technology
Chapter 9 Producing Data: Experiments
9.1 Observation versus experiment
9.2 Subjects, factors, treatments
9.3 How to experiment badly
9.4 Randomized comparative experiments
9.5 The logic of randomized comparative experiments
9.6 Cautions about experimentation
9.7 Matched pairs and other block designs
Chapter 10 Data Ethics*
10.1 Institutional review boards
10.2 Informed consent
10.3 Confidentiality
10.4 Clinical trials
10.5 Behavioral and social science experiments
Chapter 11 Producing Data: Part II Review
Part II summary
Test yourself
Supplementary exercises
PART III: FROM DATA PRODUCTION TO INFERENCE
Chapter 12 Introducing Probability
12.1 The idea of probability
12.2 The search for randomness*
12.3 Probability models
12.4 Probability rules
12.5 Discrete probability models
12.6 Continuous probability models
12.7 Random variables
12.8 Personal probability*
Chapter 13 General Rules of Probability*
13.1 The general addition rule
13.2 Independence and the multiplication rule
13.3 Conditional probability
13.4 The general multiplication rule
13.5 Showing events are independent
13.6 Tree diagrams
13.7 Bayes' rule*
Chapter 14 Binomial Distributions*
14.1 The binomial setting and binomial distributions
14.2 Binomial distributions in statistical sampling
14.3 Binomial probabilities
14.4 Using technology
14.5 Binomial mean and standard deviation
14.6 The Normal approximation to binomial distributions
Chapter 15 Sampling Distributions
15.1 Parameters and statistics
15.2 Statistical estimation and the law of large numbers
15.3 Sampling distributions
15.4 The sampling distribution of x
15.5 The central limit theorem
15.6 Sampling distributions and statistical significance*
Chapter 16 Confidence Intervals: The Basics
16.1 The reasoning of statistical estimation
16.2 Margin of error and confidence level
16.3 Confidence intervals for a population mean
16.4 How confidence intervals behave
Chapter 17 Tests of Significance: The Basics
17.1 The reasoning of tests of significance
17.2 Stating hypotheses
17.3 P-value and statistical significance
17.4 Tests for a population mean
17.5 Significance from a table*
Chapter 18 Inference in Practice
18.1 Conditions for inference in practice
18.2 Cautions about confidence intervals
18.3 Cautions about significance tests
18.4 Planning studies: Sample size for confidence intervals
18.5 Planning studies: The power of a statistical test*
Chapter 19 From Data Production to Inference: Part III Review
Part III Summary
Review Exercises
Test Yourself
Supplementary Exercises
PART IV: INFERENCE ABOUT VARIABLES
Chapter 20 Inference about a Population Mean
20.1 Conditions for inference about a mean
20.2 The t distributions
20.3 The one-sample t confidence interval
20.4 The one-sample t test
20.5 Using technology
20.6 Matched pairs t procedures
20.7 Robustness of t procedures
Chapter 21 Comparing Two Means
21.1 Two-sample problems
21.2 Comparing two population means
21.3 Two-sample t procedures
21.4 Using technology
21.5 Robustness again
21.6 Details of the t approximation*
21.7 Avoid the pooled two-sample t procedures*
21.8 Avoid inference about standard deviations*
Chapter 22 Inference about a Population Proportion
22.1 The sample proportion
22.2 Large-sample confidence intervals for a proportion
22.3 Choosing the sample size
22.4 Significance tests for a proportion
22.5 Plus four confidence intervals for a proportion*
Chapter 23 Comparing Two Proportions
23.1 Two-sample problems: Proportions
23.2 The sampling distribution of a difference between proportions
23.3 Large-sample confidence intervals for comparing proportions
23.4 Using technology
23.5 Significance tests for comparing proportions
23.6 Plus four confidence intervals for comparing proportions*
Chapter 24 Inference about Variables: Part IV Review
Part IV summary
Test yourself
Supplementary exercises
PART V: INFERENCE ABOUT RELATIONSHIPS
Chapter 25 Two Categorical Variables: The Chi-Square Test
25.1 Two-way tables
25.2 The problem of multiple comparisons
25.3 Expected counts in two-way tables
25.4 The chi-square test statistic
25.5 Using technology
25.6 The chi-square distributions
25.7 Cell counts required for the chi-square test
25.8 Uses of the chi-square test: Independence and homogeneity
25.9 The chi-square test for goodness of fit*
Chapter 26 Inference for Regression
26.1 Conditions for regression inference
26.2 Estimating the parameters
26.3 Using technology
26.4 Testing the hypothesis of no linear relationship
26.5 Testing lack of correlation
26.6 Confidence intervals for the regression slope
26.7 Inference about prediction
26.8 Checking the conditions for inference
Chapter 27 One-Way Analysis of Variance:
Comparing Several Means
27.1 Comparing several means
27.2 The analysis of variance F test
27.3 Using technology
27.4 The idea of analysis of variance
27.5 Conditions for ANOVA
27.6 F distributions and degrees of freedom
27.7 Follow-up analysis: Tukey pairwise multiple comparisons
27.8 Some details of ANOVA*
[Back matter print text]
Notes and Data Sources
Tables
TABLE A Standard Normal probabilities
TABLE B Random digits
TABLE C t distribution critical values
TABLE D Chi-square distribution critical values
TABLE E Critical values of the correlation r
Answers to Selected Exercises
Index
PART VI: OPTIONAL COMPANION CHAPTERS
(available online)
Chapter 28 Nonparametric Tests
28.1 Comparing two samples: The Wilcoxon rank sum test
28.2 The Normal approximation for W
28.3 Using technology
28.4 What hypotheses does Wilcoxon test?
28.5 Dealing with ties in rank tests
28.6 Matched pairs: The Wilcoxon signed rank test
28.7 The Normal approximation for W+
28.8 Dealing with ties in the signed rank test
28.9 Comparing several samples: The Kruskal-Wallis test
28.10 Hypotheses and conditions for the Kruskal-Wallis test
28.11 The Kruskal-Wallis test statistic
Chapter 29 Multiple Regression
29.1 Parallel regression lines
29.2 Estimating parameters
29.3 Using technology
29.4 Inference for multiple regression
29.5 Interaction
29.6 The multiple linear regression model
29.7 The woes of regression coefficients
29.8 A case study for multiple regression
29.9 Inference for regression parameters
29.10 Checking the conditions for inference
Chapter 30 More about Analysis of Variance
30.1 Beyond one-way ANOVA
30.2 Two-way ANOVA: Conditions, main effects, and interaction
30.3 Inference for two-way ANOVA
30.4 Some details of two-way ANOVA*
Chapter 31 Statistical Process Control
31.1 Processes
31.2 Describing processes
31.3 The idea of statistical process control
31.4 x charts for process monitoring
31.5 s charts for process monitoring
31.6 Using control charts
31.6 Setting up control charts
31.7 Comments on statistical control
31.8 Don't confuse control with capability!
31.9 Control charts for sample proportions
31.10 Control limits for p charts
Chapter 32 Resampling: Permutation Tests and the Bootstrap
32.1 Randomization in experiments as a basis for inference
32.2 Permutation tests for comparing two treatments with software
32.3 Generating bootstrap samples
32.4 Bootstrap standard errors and confidence intervals

The Basic Practice of Statistics
Eighth Edition| 2018
David S. Moore; William I. Notz; Michael Fligner
Authors

David S. Moore
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 AB from Princeton and his PhD 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
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
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.

The Basic Practice of Statistics
Eighth Edition| 2018
David S. Moore; William I. Notz; Michael Fligner
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David S. Moore; William I. Notz; Michael A. Fligner | Eighth Edition | ©2018 | ISBN:9781319057046
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The Basic Practice of Statistics
Eighth Edition| 2018
David S. Moore; William I. Notz; Michael Fligner
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