Giáo trình The basic practice of statistics 9e by moore

BRIEF CONTENTSChapter 0 Getting Started Part I Exploring Data EXPLORING DATA: Variables and Distributions Chapter 1 Picturing Distributions with Graphs Chapter 2 Describing Distributions

The Basic Practice of Statistics

The Basic Practice of StatisticsNinth Edition

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BRIEF CONTENTS

Chapter 0 Getting Started

Part I Exploring Data

EXPLORING DATA: Variables and Distributions

Chapter 1 Picturing Distributions with Graphs

Chapter 2 Describing Distributions with Numbers

Chapter 3 The Normal Distributions

EXPLORING DATA: Relationships

Chapter 4 Scatterplots and Correlation

Chapter 5 Regression

Chapter 6 Two-Way Tables*

Chapter 7 Exploring Data: Part I Review

Part II Producing Data

PRODUCING DATA

Chapter 8 Producing Data: Sampling

Chapter 9 Producing Data: Experiments

Chapter 10 Data Ethics*

Chapter 11 Producing Data: Part II Review

Part III From Data Production to Inference

PROBABILITY AND SAMPLING DISTRIBUTIONS

Chapter 12 Introducing Probability

Chapter 13 General Rules of Probability*

Chapter 14 Binomial Distributions*

Chapter 15 Sampling Distributions

FOUNDATIONS OF INFERENCE

Chapter 16 Confidence Intervals: The Basics

Chapter 17 Tests of Significance: The Basics

Chapter 18 Inference in Practice

Chapter 19 From Data Production to Inference: Part III Review

Part IV Inference about Variables

QUANTITATIVE RESPONSE VARIABLE

Chapter 20 Inference about a Population Mean

Chapter 21 Comparing Two Means

CATEGORICAL RESPONSE VARIABLE

Chapter 22 Inference about a Population Proportion

Chapter 23 Comparing Two Proportions

Chapter 24 Inference about Variables: Part IV Review

Part V Inference about Relationships

INFERENCE ABOUT RELATIONSHIPS

Chapter 25 Two Categorical Variables: The Chi-Square Test

Chapter 26 Inference for Regression

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

Part VI Optional Companion Chapters

(Available Online)

Chapter 28 Nonparametric Tests

Chapter 29 Multiple Regression*

Chapter 30 Two-Way Analysis of Variance

Chapter 31 Statistical Process Control

Chapter 32 Resampling: Permutation Tests and the Bootstrap

*Starred material is optional and can be skipped without loss of continuity

1 Chapter 0 Getting Started

1 0.1 How the Data Were Obtained Matters

2 0.2 Always Look at the Data

3 0.3 Variation Is Everywhere

4 0.4 What Lies Ahead in This Book

6 Part I Exploring Data

1 Chapter 1 Picturing Distributions with Graphs

1 1.1 Individuals and Variables

2 1.2 Categorical Variables: Pie Charts and Bar Graphs

3 1.3 Quantitative Variables: Histograms

4 1.4 Interpreting Histograms

5 1.5 Quantitative Variables: Stemplots

6 1.6 Time Plots

2 Chapter 2 Describing Distributions with Numbers

1 2.1 Measuring Center: The Mean

2 2.2 Measuring Center: The Median

3 2.3 Comparing the Mean and the Median

4 2.4 Measuring Variability: The Quartiles

5 2.5 The Five-Number Summary and Boxplots

6 2.6 Spotting Suspected Outliers and Modified Boxplots*

7 2.7 Measuring Variability: The Standard Deviation

8 2.8 Choosing Measures of Center and Variability

9 2.9 Examples of Technology

10 2.10 Organizing a Statistical Problem

3 Chapter 3 The Normal Distributions

1 3.1 Density Curves

2 3.2 Describing Density Curves

3 3.3 Normal Distributions

4 3.4 The 68–95–99.7 Rule

5 3.5 The Standard Normal Distribution

6 3.6 Finding Normal Proportions

7 3.7 Using the Standard Normal Table

8 3.8 Finding a Value Given a Proportion

4 Chapter 4 Scatterplots and Correlation

1 4.1 Explanatory and Response Variables

2 4.2 Displaying Relationships: Scatterplots

3 4.3 Interpreting Scatterplots

4 4.4 Adding Categorical Variables to Scatterplots

5 4.5 Measuring Linear Association: Correlation

6 4.6 Facts about Correlation

7 5.7 Cautions about Correlation and Regression

8 5.8 Association Does Not Imply Causation

9 5.9 Correlation, Prediction, and Big Data*

6 Chapter 6 Two-Way Tables*

1 6.1 Marginal Distributions

2 6.2 Conditional Distributions

3 6.3 Simpson’s Paradox

7 Chapter 7 Exploring Data: Part I Review

1 Part I Skills Review

2 Test Yourself

3 Supplementary Exercises

4 Online Data for Additional Analyses

7 Part II Producing Data

1 Chapter 8 Producing Data: Sampling

1 8.1 Population versus Sample

2 8.2 How to Sample Badly

3 8.3 Simple Random Samples

4 8.4 Trustworthiness of Inference from Samples

5 8.5 Other Sampling Designs

6 8.6 Cautions about Sample Surveys

7 8.7 The Impact of Technology

2 Chapter 9 Producing Data: Experiments

1 9.1 Observation Versus Experiment

2 9.2 Subjects, Factors, and Treatments

3 9.3 How to Experiment Badly

4 9.4 Randomized Comparative Experiments

5 9.5 The Logic of Randomized Comparative Experiments

6 9.6 Cautions About Experimentation

7 9.7 Matched Pairs and Other Block Designs

3 Chapter 10 Data Ethics*

1 10.1 Institutional Review Boards

2 10.2 Informed Consent

3 10.3 Confidentiality

4 10.4 Clinical Trials

5 10.5 Behavioral and Social Science Experiments

4 Chapter 11 Producing Data: Part II Review

1 Part II Skills Review

2 Test Yourself

3 Supplementary Exercises

8 Part III From Data Production to Inference

1 Chapter 12 Introducing Probability

1 12.1 The Idea of Probability

2 12.2 The Search for Randomness*

3 12.3 Probability Models

4 12.4 Probability Rules

5 12.5 Finite Probability Models

6 12.6 Continuous Probability Models

7 12.7 Random Variables

8 12.8 Personal Probability*

2 Chapter 13 General Rules of Probability*

1 13.1 The General Addition Rule

2 13.2 Independence and the Multiplication Rule

3 13.3 Conditional Probability

4 13.4 The General Multiplication Rule

5 13.5 Showing That Events Are Independent

6 13.6 Tree Diagrams

7 13.7 Bayes’ Rule*

3 Chapter 14 Binomial Distributions*

1 14.1 The Binomial Setting and Binomial Distributions

2 14.2 Binomial Distributions in Statistical Sampling

3 14.3 Binomial Probabilities

4 14.4 Examples of Technology

5 14.5 Binomial Mean and Standard Deviation

6 14.6 The Normal Approximation to Binomial Distributions

4 Chapter 15 Sampling Distributions

1 15.1 Parameters and Statistics

2 15.2 Statistical Estimation and the Law of Large Numbers

3 15.3 Sampling Distributions

4 15.4 The Sampling Distribution of x¯

5 15.5 The Central Limit Theorem

6 15.6 Sampling Distributions and Statistical Significance*

5 Chapter 16 Confidence Intervals: The Basics

1 16.1 The Reasoning of Statistical Estimation

2 16.2 Margin of Error and Confidence Level

3 16.3 Confidence Intervals for a Population Mean

4 16.4 How Confidence Intervals Behave

6 Chapter 17 Tests of Significance: The Basics

1 17.1 The Reasoning of Tests of Significance

2 17.2 Stating Hypotheses

3 17.3 P-Value and Statistical Significance

4 17.4 Tests for a Population Mean

5 17.5 Significance from a Table*

7 Chapter 18 Inference in Practice

1 18.1 Conditions for Inference in Practice

2 18.2 Cautions about Confidence Intervals

3 18.3 Cautions about Significance Tests

4 18.4 Planning Studies: Sample Size for Confidence Intervals

5 18.5 Planning Studies: The Power of a Statistical Test of Significance*

8 Chapter 19 From Data Production to Inference: Part III Review

1 Part III Skills Review

2 Test Yourself

3 Supplementary Exercises

9 Part IV Inference about Variables

1 Chapter 20 Inference about a Population Mean

1 20.1 Conditions for Inference about a Mean

2 20.2 The t Distributions

3 20.3 The One-Sample t Confidence Interval

4 20.4 The One-Sample t Test

6 21.6 Details of the t Approximation*

7 21.7 Avoid the Pooled Two-Sample t Procedures*

8 21.8 Avoid Inference about Standard Deviations*

3 Chapter 22 Inference about a Population Proportion

1 22.1 The Sample Proportion p^

2 22.2 Large-Sample Confidence Intervals for a Proportion

3 22.3 Choosing the Sample Size

4 22.4 Significance Tests for a Proportion

5 22.5 Plus Four Confidence Intervals for a Proportion*

4 Chapter 23 Comparing Two Proportions

1 23.1 Two-Sample Problems: Proportions

2 23.2 The Sampling Distribution of a Difference between Proportions

3 23.3 Large-Sample Confidence Intervals for Comparing Proportions

4 23.4 Examples of Technology

5 23.5 Significance Tests for Comparing Proportions

6 23.6 Plus Four Confidence Intervals for Comparing Proportions*

5 Chapter 24 Inference about Variables: Part IV Review

1 Part IV Skills Review

2 Test Yourself

3 Supplementary Exercises

10 Part V Inference about Relationships

1 Chapter 25 Two Categorical Variables: The Chi-Square Test

1 25.1 Two-Way Tables

2 25.2 The Problem of Multiple Comparisons

3 25.3 Expected Counts in Two-Way Tables

4 25.4 The Chi-Square Statistic

5 25.5 Examples of Technology

6 25.6 The Chi-Square Distributions

7 25.7 Cell Counts Required for the Chi-Square Test

8 25.8 Uses of the Chi-Square Test: Independence and Homogeneity

9 25.9 The Chi-Square Test for Goodness of Fit*

2 Chapter 26 Inference for Regression

1 26.1 Conditions for Regression Inference

2 26.2 Estimating the Parameters

3 26.3 Examples of Technology

4 26.4 Testing the Hypothesis of No Linear Relationship

5 26.5 Testing Lack of Correlation

6 26.6 Confidence Intervals for the Regression Slope

7 26.7 Inference about Prediction

8 26.8 Checking the Conditions for Inference

3 Chapter 27 One-Way Analysis of Variance: Comparing Several Means

1 27.1 Comparing Several Means

2 27.2 The Analysis of Variance F Test

3 27.3 Using Technology

4 27.4 The Idea of Analysis of Variance

5 27.5 Conditions for ANOVA

6 27.6 F Distributions and Degrees of Freedom

7 27.7 Follow-up Analysis: Tukey Pairwise Multiple Comparisons

8 27.8 Some Details of ANOVA*

11 Notes and Data Sources

12 Tables

TABLE A Standard Normal Cumulative Proportions

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

13 Answers to Selected Exercises

14 Index

15 Part VI Optional Companion Chapters

(Available Online)

1 Chapter 28 Nonparametric Tests

1 28.1 Comparing Two Samples: The Wilcoxon Rank Sum Test

2 28.2 The Normal Approximation for W

3 28.3 Examples of Technology

4 28.4 What Hypotheses Does Wilcoxon Test?

5 28.5 Dealing with Ties in Rank Tests

6 28.6 Matched Pairs: The Wilcoxon Signed Rank Test

7 28.7 The Normal Approximation for W+

8 28.8 Dealing with Ties in the Signed Rank Test

9 28.9 Comparing Several Samples: The Kruskal–Wallis Test

10 28.10 Hypotheses and Conditions for the Kruskal–Wallis Test

11 28.11 The Kruskal–Wallis Test Statistic

2 Chapter 29 Multiple Regression*

1 29.1 Adding a Categorical Variable in Regression

2 29.2 Estimating Parameters

3 29.3 Examples of Technology

4 29.4 Inference for Multiple Regression

5 29.5 Interaction

6 29.6 A Model with Two Regression Lines

7 29.7 The General Multiple Linear Regression Model

8 29.8 Correlations between Explanatory Variables

9 29.9 A Case Study for Multiple Regression

10 29.10 Inference for Regression Parameters

11 29.11 Checking the Conditions for Inference

3 Chapter 30 Two-Way Analysis of Variance

1 30.1 Beyond One-Way ANOVA

2 30.2 Two-Way ANOVA: Conditions, Main Effects, and Interaction

3 30.3 Inference for Two-Way ANOVA

4 30.4 Some Details of Two-Way ANOVA*

4 Chapter 31 Statistical Process Control

1 31.1 Processes

2 31.2 Describing Processes

3 31.3 The Idea of Statistical Process Control

4 31.4 x¯ Charts for Process Monitoring

5 31.5 s Charts for Process Monitoring

6 31.6 Using Control Charts

7 31.7 Setting Up Control Charts

8 31.8 Comments on Statistical Control

9 31.9 Don’t Confuse Control with Capability

10 31.10 Control Charts for Sample Proportions

11 31.11 Control Limits for p Charts

5 Chapter 32 Resampling: Permutation Tests and the Bootstrap

1 32.1 Randomization in Experiments as a Basis for Inference

2 32.2 Permutation Tests for Two Treatments with Software

3 32.3 Generating Bootstrap Samples

4 32.4 Bootstrap Standard Errors and Confidence Intervals

*Starred material is optional and can be skipped without loss of continuity

WHY DID YOU DO THAT?

The Authors Answer Questions about The Basic Practice of Statistics

Welcome to the ninth edition of The Basic Practice of Statistics As the title suggests, this text

provides an introduction to the practice of statistics that aims to equip students to carry out commonstatistical procedures and to follow statistical reasoning in their fields of study and in their futureemployment

There is no single best way to organize our presentation of statistics to beginners That said, our

choices reflect thinking about both content and pedagogy Here are comments on several frequently

asked questions about the order and selection of material in The Basic Practice of Statistics.

Why Did You Write The Basic Practice of Statistics?

Several factors influenced the writing of The Basic Practice of Statistics Easy-to-use statistical

software with graphical tools made it possible for students to explore and analyze data on their own

Statistics educators recognized that actually doing statistics—exploring data, analyzing data, thinking

about what the data are telling us, and assessing the validity of the conclusions we make from data—

is an effective way to learn statistics Teachers also recognized the importance of using real data

from actual studies to reinforce the fact that statistics is invaluable for answering real-world

questions Finally, an introductory course in statistics should expose students to how statistics is

actually practiced by researchers At the time of the writing of the first edition, few, if any, textbooksfor courses intended for students with only college algebra as the mathematics prerequisite

incorporated these ideas

With this in mind, The Basic Practice of Statistics was designed to reflect 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 Basic Practice of Statistics was also designed to be accessible

to college and university students with limited quantitative background—just “algebra” in the sense ofbeing able to read and use simple equations

Why Should I Use The Basic Practice of Statistics to Teach an

Introductory Statistics Course?

The Basic Practice of Statistics is based on three principles: balanced content, experience with data,

and the importance of ideas These principles are widely accepted by statisticians concerned aboutteaching 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 GAISE guidelines include six recommendations for introductory statistics The content, coverage,

and features of The Basic Practice of Statistics are closely aligned to these recommendations:

1 Teach statistical thinking.

Teach statistics as an investigative process of problem solving and decision making In The Basic Practice of Statistics, we present a four-step process for solving statistical

problems This begins by stating the practical question to be answered in the context of areal-world setting and ends with a practical conclusion, often a decision to be made, in thesetting of the real-world problem The process is illustrated in the text by revisiting datafrom a study in a series of examples or exercises Different aspects of the data are

investigated in different examples and exercises, with the ultimate goal of making somedecision based on what has been learned

Give students experience with multivariable thinking The Basic Practice of Statistics

exposes students to multivariate thinking early in the book Chapters 4, 5, and 6 introducestudents to methods for exploring bivariate data In Chapter 7, we include online data withmany variables, inviting students to explore aspects of these data In Chapter 9, we discussthe importance of identifying the many variables that can affect a response and includingthem in the design of an experiment and the interpretation of the results In Part V, we

introduce students to formal methods of inference for bivariate data, and in the online

supplemental chapters, we discuss multiple regression, two-way ANOVA, and statisticalprocess control

2 Focus on conceptual understanding A first course in statistics introduces many skills, from

making a stemplot and calculating a correlation to choosing and carrying out a significance test

In practice (even if not always in the course), calculations and graphs are automated Moreover,anyone who makes serious use of statistics will need some specific procedures not taught in

their college statistics course The Basic Practice of Statistics, therefore, emphasizes

conceptual understanding by making clear the larger patterns and big ideas of statistics—not inthe abstract but in the context of learning specific skills and working with specific data Many ofthe big ideas are summarized in graphical outlines Three of the most useful of these appear

opposite the title page Formulas without guiding principles do students little good once the finalexam is past, so it is worth the time to slow down a bit and explain the ideas

3 Integrate real data with a context and a purpose The study of statistics is supposed to help

students work with data in their varied academic disciplines and in their unpredictable later

employment Students learn to work with data by working with data The Basic Practice of

Statistics is full of data from many fields of study and from everyday life Data are more than

mere numbers: they are numbers with a context that should play a role in making sense of the

numbers and in stating conclusions Examples and exercises in The Basic Practice of Statistics,

though intended for beginners, use real data and give enough background to allow students toconsider the meaning of their calculations

4 Foster active learning Fostering active learning is the business of the teacher, though an

emphasis on working with data helps To this end, we have created interactive applets to our

specifications that are available online These are designed primarily to help in learning

statistics rather than in doing statistics We suggest using selected applets for classroom

demonstrations even if you do not ask students to work with them The Correlation and

Regression, Confidence Intervals, and P-Value of a Test of Significance applets, for example,

convey core ideas more clearly than any amount of chalk and talk

For each chapter (except the review chapters), web exercises are provided online Our intent is

to take advantage of the fact that most undergraduates are web savvy These exercises requirestudents to search the web for either data or statistical examples and then evaluate what theyfind Teachers can use these as classroom activities or assign them as homework projects

5 Use technology to explore concepts and analyze data Automating calculations increases

students’ ability to complete problems, reduces their frustration, and helps them concentrate onideas and problem recognition rather than mechanics At a minimum, students should have a

“two-variable statistics” calculator with functions for correlation and the least-squares

regression line as well as for the mean and standard deviation

Many instructors will take advantage of more elaborate technology, as ASA/MAA and GAISErecommend And many students who don’t use technology in their college statistics course will

find themselves using (for example) Excel on the job The Basic Practice of Statistics does not

assume or require use of software except in Part V, where the work is otherwise too tedious Itdoes accommodate software use and provides students with knowledge that will enable them toread and use output from almost any source There are regular “Examples of Technology”

sections throughout the text Each of these sections displays and comments on output from thesame three technologies, representing graphing calculators (the Texas Instruments TI-83 or TI-84), spreadsheets (Microsoft Excel), and statistical software (JMP, Minitab, R, and CrunchIt!).The output always concerns one of the main teaching examples so that students can compare textand output

6 Use assessments to improve and evaluate student learning Within chapters, a few “Apply

Your Knowledge” exercises follow each new idea or skill for a quick check of basic mastery—and also to mark off digestible bites of material Each of the first four parts of the book endswith a review chapter that includes a point-by-point outline of skills learned, problems studentscan use to test themselves, and several supplementary exercises (Instructors can choose to coverany or none of the chapters in Part V, so each of these chapters includes a skills outline.) Thereview chapters present supplemental exercises without the “I just studied that” context, thusasking for another level of learning We think it is helpful to assign some supplemental

exercises Many instructors will find that the review chapters appear at the right points for exam review Students can use the “Test Yourself” questions to review, self-assess, and preparefor exams In addition, assessment materials in the form of a test bank and quizzes are availableonline

pre-Why Did You Choose to Order Topics as Listed in the Book?

There are good pedagogical reasons for beginning with data analysis (Chapters 1 through 7), thenmoving to data production (Chapters 8 through 11), and then to probability and inference (Chapters

12 through 27) In studying data analysis, students learn useful skills immediately and get over some

of their fear of statistics Data analytics is much in the media these days, and by discussing data

analysis, instructors can link the course material to the current interest in data analytics Data analysis

is a necessary preliminary to inference in practice because inference requires clean data Designeddata production is the surest foundation for inference, and the deliberate use of chance in randomsampling and randomized comparative experiments motivates the study of probability in a course that

emphasizes data-oriented statistics The Basic Practice of Statistics gives a full presentation of basic

probability and inference (16 of the 27 chapters in the printed text) but places it in the context ofstatistics as a whole

Why Does the Distinction between Population and Sample Not

Appear in Part I?

There is more to statistics than inference In fact, statistical inference is appropriate only in ratherspecial circumstances The chapters in Part I present tools and tactics for describing data—any data.These tools and tactics do not depend on the idea of inference from sample to population Many datasets in these chapters (for example, the several sets of data about the 50 states) do not lend

themselves to inference because they represent an entire population Likewise, many modern big datasets are also viewed as information about an entire population, for which formal inference may not beappropriate John Tukey of Bell Labs and Princeton, the philosopher of modern data analysis, insisted

that the population–sample distinction be avoided when it is not relevant He used the word batch for

data sets in general We see no need for a special word, but we think Tukey was right

Why Not Begin with Data Production?

We prefer to begin with data exploration (Part I), as most students will use statistics mainly in

settings other than planned research studies in their future employment We place the design of dataproduction (Part II) after data analysis to emphasize that data-analytic techniques apply to any data.However, it is equally reasonable to begin with data production; the natural flow of a planned study

is from design to data analysis to inference Because instructors have strong and differing opinions onthis question, these two topics are now the first two parts of the book, with the text written so that itmay be started with either Part I or Part II while maintaining the continuity of the material

Another reason for beginning with data exploration is to give students experience exploring data andthinking about how to interpret what they discover This experience provides a context for how dataproduction affects the reliability of conclusions one might draw from data

Why Do Normal Distributions Appear in Part I?

Density curves such as the Normal curves are just another tool to describe the distribution of a

quantitative variable, along with stemplots, histograms, and boxplots Professional statistical

software offers to make density curves from data just as it offers histograms We prefer not to suggestthat this material is essentially tied to probability, as the traditional order does And we find it helpful

to break up the indigestible lump of probability that troubles students so much Meeting Normal

distributions early does this and strengthens the “probability distributions are like data distributions”way of approaching probability when we get there

Why Not Delay Correlation and Regression Until Late in the

Course, as Was Traditional?

The Basic Practice of Statistics begins by offering experience working with data and gives a

conceptual structure for this nonmathematical, but essential, part of statistics Students profit frommore experience with data early and from seeing the conceptual structure worked out in relationshipsamong variables as well as in describing single-variable data Correlation and least-squares

regression are very important descriptive tools and are often used in settings where there is no

population–sample distinction, such as studies of all of a firm’s employees Perhaps most

importantly, The Basic Practice of Statistics asks students to think about what kind of relationship

lies behind the data (confounding, lurking variables, association not implying causation, and so on)without overwhelming them with the demands of formal inference methods Inference in the

correlation and regression setting is a bit complex, demands software, and often comes right at theend of the course We find that delaying all mention of correlation and regression to that point oftenmeans that students don’t master the basic uses and properties of these methods We consider

Chapters 4 and 5 (correlation and regression) essential and Chapter 26 (regression inference)

optional

Why Use the z Procedures for a Population Mean to Introduce the

Reasoning of Inference?

This is a pedagogical issue, not a question of statistics in practice The two most popular choices for

introducing inference are z for a mean and z for a proportion (Another option is resampling and

permutation tests We have included material on these topics but have not used them to introduceinference.)

We find z for means quite accessible to students Positively, we can say up front that we are going to

explore the reasoning of inference in the overly simple setting described in the box on page 367 titled

“Simple Conditions for Inference about a Mean.” As this box suggests, assuming an exactly Normal

population and a true simple random sample are as unrealistic as known s All the issues of practice

—robustness against lack of Normality and application when the data aren’t an SRS as well as the

need to estimate s—are put off until, with the reasoning in hand, we discuss the practically useful t

procedures This separation of initial reasoning from messier practice works well

Negatively, starting with inference for p introduces many side issues: no exact Normal sampling

distribution but a Normal approximation to a discrete distribution; use of p^ in both the numerator and

denominator of the test statistic to estimate both the parameter p and p^’s own standard deviation;

loss of the direct link between test and confidence interval; and the need to avoid small and moderatesample sizes because the Normal approximation for the test is quite unreliable

There are advantages to starting with inference for p Starting with z for means takes a fair amount of time, and the ideas need to be rehashed with the introduction of the t procedures Many instructors

face pressure from client departments to cover a large amount of material in a single semester

Eliminating coverage of the “unrealistic” z for means with known variance enables instructors to

cover additional, more realistic applications of inference Also, many instructors believe that

proportions are simpler and more familiar to students than means

Why Didn’t You Cover Topic X?

Introductory texts ought not to be encyclopedic We chose topics on two grounds: they are the mostcommonly used in practice, and they are suitable vehicles for learning broader statistical ideas

Students who have completed the core of the book, Chapters 1 through 12 and 15 through 24, willhave little difficulty moving on to more elaborate methods Chapters 25 through 27 offer a choice ofslightly more advanced topics, as do the optional supplemental chapters, available online

Why Are Some Chapters and Sections Listed as Optional?

Many users have requested that we include the content listed as optional However, as noted above,many instructors face pressure from client departments to cover many topics in a single semester Wehave identified some material that can safely be omitted because it is not required for later parts ofthe book Instructors can cover this optional content if they wish, but they can also omit it in order tocover topics that client departments have requested

The content we designate as optional is not less important than other material in the book For

example, many instructors will want to cover Chapters 6 and 25 because they consider relationshipsbetween categorical variables an essential topic for their students

We have enjoyed the opportunity to once again rethink how to help beginning students achieve a

practical grasp of basic statistics What students actually learn is not identical to what we teachersthink we have “covered,” so the virtues of concentrating on the essentials are considerable We hope

this new edition of The Basic Practice of Statistics offers a mix of concrete skills and clearly

explained concepts that will help many teachers guide their students toward useful knowledge

Empowering Problem Solving and Real-World Decision Making

Now available with Macmillan’s new, ground-breaking online learning platform Achieve, the ninth

edition of The Basic Practice of Statistics teaches statistical thinking through an investigative

process of problem solving with pedagogy designed to help students of all levels Examples and

exercises from a wide variety of topic areas use current, real data to provide students with insightinto how data is used to make decisions in the real world

Achieve for The Basic Practice of Statistics connects the book’s trusted Four-Step problem-solving

approach and real-world examples to rich digital resources that foster understanding and facilitate thepractice of statistics The tools in Achieve support learning before, during, and after class for studentsand equip instructors with class performance analytics in an easy-to-use interface

Overview of key features

Support for Learners on Every Page

Four-Step Problem-Solving examples guide students through the Four-Step process for working

through statistical problems: State, Plan, Solve, Conclude Students are instructed to apply thisprocess in designated exercises

Apply Your Knowledge exercises at the end of each section encourage students to read actively

and to cement new concepts by applying them as they learn

Examples of Technology, located where most appropriate, display and comment on the output

from popular statistical software applications (notably Excel, Minitab, JMP, and R) and TI

83/84 graphing calculators in the context of worked examples Students learn to interpret outputfrom any standard statistical package

Definition and Theorem Boxes in the text alert students to key concepts, terms, and procedures Caution Boxes warn students of common mistakes or misconceptions.

Statistics in Your World margin notes further connect statistics topics to the real world,

highlighting interesting examples and applications from a variety of fields

The main themes of the text are strongly aligned to the GAISE guidelines (from the Guidelines

in Assessment and Instruction in Statistics Education College Report)

New to the Ninth Edition

Examples and exercises clearly emphasize reaching conclusions and making decisions based

on data exploration and statistical inference

Chapter Summaries are in concise list form, and Skills Reviews (in Review Chapters) refer to

relevant chapter sections to help students check their knowledge and review for exams.

Data in examples and exercises have been updated for relevance, and new examples and

exercises explore contemporary issues such as social media usage.

Displayed in boldface type, key terms are clearly defined in running text or in the margin, to

build understanding without focusing on vocabulary

Highlight Four-Step Problem-Solving

Expanded in the Ninth Edition

Equips Students to Solve Complex Statistical Problems

Recognizing which approach to take and how to get started on a problem are often challenging forstatistics students David Moore and William Notz reduce student stress and support learning by using

a problem-solving framework throughout The Basic Practice of Statistics.

The process is as follows State: What is the practical question, in the context of the real-world

setting? Plan: What specific statistical operations does this problem call for? Solve: Make the graphsand carry out the calculations needed for this problem Conclude: Give your practical conclusion inthe setting of the real-world problem

“The Basic Practice of Statistics has a great history and improves with each version [It]

always has a polished presentation with excellent explanations.”

—Patricia Buchanan, Professor, Pennsylvania State University

Achieve is the culmination of years of development work put toward creating the most powerfulonline learning tool for statistics students It houses all of our renowned assessments, multimediaassets, e-books, and instructor resources in a powerful new platform

Achieve supports educators and students throughout the full range of instruction, including assetssuitable for pre-class preparation, in-class active learning, and post-class study and assessment The

pairing of a powerful new platform with outstanding statistics content provides an unrivaled learningexperience.

For more information or to sign up for a demonstration of Achieve, contact your local Macmillanrepresentative or visit macmillanlearning.com/achieve

Robust tutorial-style assessment tools in Achieve help students develop problem-solving and

statistical reasoning skills Achieve contains more than 3,000 assessment questions designed for bothpre-class foundational learning and post-class homework For select questions, our formative

assessment environment responds to students’ incorrect answers with feedback to guide their study.This Socratic feedback mechanism emulates the office-hours experience, encouraging students tothink critically about their identified misconceptions Students make the most out of homework withAchieve’s hallmark hints, detailed feedback, and fully worked solutions

LearningCurve Adaptive Quizzing

LearningCurve’s game-like quizzing motivates students to engage with the course content, and

reporting tools help teachers get a handle on what their class needs

Students earn points for correct answers and receive immediate feedback on incorrect answers Atany time during the activity, students can access a study plan with links to additional study tools.Questions are tagged to sections of the e-book to provide comprehensive coverage and easy-to-findhelp The quiz adapts to each student’s needs based on performance, automatically providing morequestions on topics where the student is struggling Performance can be tracked by learning

objectives, to give instructors actionable information about topics that need additional emphasis

Using the Power of Computing to Work with Data

Students are provided with a wealth of multimedia resources and opportunities for practice, to coachthem toward fuller conceptual understanding and proficiency in solving problems

Video Technology Manuals are brief, focused instructional videos that show students how to useparticular applications (Excel, Minitab, R, etc.) to perform specific tests and other calculations

covered in The Basic Practice of Statistics.

Powerful analytics, viewable in an elegant dashboard, offer instructors a window into student

progress Achieve gives you the insight to address students’ weaknesses and misconceptions beforethey struggle on a test

Achieve’s Rich Digital Resources

Multimedia resources, such as interactives and videos, serve as an extension of the carefully

constructed examples and exercises in the text

A variety of videos in Achieve provide additional exposure to key concepts and examples All

videos are narrated and close-captioned, and many include linked assessment questions Video typesinclude whiteboard-style problem-solving videos, animated lecture videos, software assistancevideos, and documentary-style video lessons that illustrate real-world scenarios involving statistics

Statistical Applets, referenced and displayed in the text, are visual interactives originally designed byDavid Moore This feature enables students to manipulate data in calculations and see the resultsgraphically Applets are associated with questions in Achieve that assess students’ comprehension.Achieve includes updated versions of the applets, powered by Desmos.

A variety of instructor resources accompany the ninth edition of The Basic Practice of Statistics:

We are grateful to colleagues from two-year and four-year colleges and universities who reviewed

and commented on the previous edition of The Basic Practice of Statistics, in preparation for this

revision, or who commented on the ninth edition manuscript

Todd Burus, Eastern Kentucky University

Rita Chattopadhyay, Eastern Michigan University

Elijah Dikong, Michigan State University

Kimberly Druschel, Saint Louis University

Cathy Frey, Norwich University

Petre Ghenciu, University of Wisconsin–Stout

Billie-Jo Grant, California State Polytechnic University

Mark Hardwidge, Danville Area Community College

Lisa Kay, Eastern Kentucky University

Michael Macon, Green River Community College

Connie Marberry, Kirkwood Community College

Andrew McDougall, Montclair State University

Juli Moore, Oregon State University

Roland Moore, Florida State University

Thomas Oliveri, University of Massachusetts–Lowell

Christina Pierre, Saint Mary’s University of Minnesota

Mohammed Quasem, University of South Carolina

Joshua Roberts, Georgia Gwinnett College

N Paul Schembari, East Stroudsburg University

Hilary Seagle, Southwestern Community College

Kristi Spittler-Brown, Arkansas Tech University

Tim Swartz, Simon Fraser University

Susan Toma, Madonna University

Carol Weideman, St Petersburg College

We extend our appreciation to Sarah Seymour, Debbie Hardin, David Dietz, Katrina Mangold,Catriona Kaplan, Andy Newton, Justin Jones, Lisa Kinne, Edward Dionne, Paul Rohloff, DianaBlume, John Callahan, Vicki Tomaselli, and other publishing professionals who have contributed tothe development, design, production, and cohesiveness of this book and its online resources

Jack Miller and Mark McKibben are to be commended for their work on the full solutions manuals,

as well as the back-of-book answers and exercise evaluations—offering their backgrounds in

statistics and dedication to education John Samons went above and beyond to ensure the accuracy,flow, and consistency of the presentation in the text, back-of-book answers, and full solutions Wethank all three of you for embracing the project in the spirit of teamwork and collaboration

We also extend our gratitude to the following collaborators who contributed their expertise to the

instructor and student resources for this edition:

Nicole Dalzell, Wake Forest University, revised the Clicker Questions and the Test Bank

Terri Rizzo, Lakehead University, accuracy reviewed the Clicker Questions, Practice Quizzes,and Test Bank

Mark Gebert, University of Kentucky, revised the Lecture Slides and Practice Quizzes

Michelle Duda, Columbus State Community College, revised the Instructor’s Guide

Karen Starin, Columbus State Community College, accuracy reviewed the Instructor’s Guide

We would like to thank Robert Wolf, University of San Francisco; Eugene Komaroff, Keiser

University; Stephen Doty; and Aaron Gladish for pointing out errors in the previous edition and forhelpful suggestions

Portions of information contained in this book are printed with permission of Minitab, LLC All suchmaterial remains the exclusive property and copyright of Minitab, LLC All rights reserved

Finally, we are indebted to the many statistics teachers with whom we have discussed the teaching ofour subject over many years; to people from diverse fields with whom we have worked to understanddata; and, especially, to students whose compliments and complaints have changed and improved ourteaching Working with teachers, colleagues in other disciplines, and students constantly reminds us

of the importance of hands-on experience with data and of statistical thinking in an era when computerroutines quickly handle statistical details

David S Moore and William I Notz

ABOUT THE AUTHORS

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 an AB fromPrinceton and a 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 anelected fellow of the American Statistical Association and of the Institute of Mathematical Statisticsand an elected member of the International Statistical Institute He has served as program director forstatistics and probability at the National Science Foundation

In recent years, Professor Moore has devoted his attention to the teaching of statistics He was thecontent 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 textbooks Professor Moore has served as

president of the International Association for Statistical Education and has received the MathematicalAssociation of America’s national award for distinguished college or university teaching of

mathematics

William I Notz is Professor Emeritus at The Ohio State University He received a BS in physics

from Johns Hopkins University and a PhD in mathematics from Cornell University His first academicjob was as assistant professor in the Department of Statistics at Purdue University While there, hetaught the introductory statistics concepts course with Professor Moore and developed an interest in

statistical education Professor Notz is a co-author of the Electronic Encyclopedia of Statistical Examples and Exercises and co-author of Statistics: Concepts and Controversies His research

interests have focused on experimental design and computer experiments He is the author of severalresearch papers and of a book on the design and analysis of computer experiments William Notz is

an elected fellow of the American Statistical Association and has served as the editor of the journal

Technometrics and as editor of the Journal of Statistics Education At The Ohio State University, he

has served as the director of the Statistical Consulting Service, as acting chair of the Department ofStatistics, and as an associate dean in the College of Mathematical and Physical Sciences ProfessorNotz is a winner of Ohio State’s Alumni Distinguished Teaching Award