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ISBN: 9781319013387 | 1319013384

Edition: 9thFormat: Hardcover

Publisher: W. H. Freeman

Pub. Date: 12/15/2016

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**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.

**George P. McCabe** is the Associate Dean for Academic Affairs in the College of Science and a Professor of Statistics at Purdue University. In 1966, he received a B.S. degree in mathematics from Providence College, and in 1970 a Ph.D. in mathematical statistics from Columbia University. His entire professional career has been spent at Purdue with sabbaticals at Princeton, the Commonwealth Scientific and Industrial Research Organization in Melbourne (Australia); the University of Berne (Switzerland); the National Institute of Standards and Technology (Boulder, Colorado); and the National University of Ireland in Galway. Professor McCabe is an elected fellow of the American Association for the Advancement of Science and of the American Statistical Association; he was 1998 Chair of its section on Statistical Consulting. From 2008 to 2010, he served on the Institute of Medicine Committee on Nutrition Standards for the National School Lunch and Breakfast Programs. He has served on the editorial boards of several statistics journals, has consulted with many major corporations, and has testified as an expert witness on the use of statistics.

Professor McCabe’s research has focused on applications of statistics. Much of his recent work has been on problems of nutrition, including nutrient requirements, calcium metabolism, and bone health. He is author or coauthor of more than 160 publications in many different journals. **Bruce A. Craig** is Professor of Statistics and Director of the Statistical Consulting Service at Purdue University. He received his B.S. in mathematics and economics from Washington University in St. Louis and his PhD in statistics from the University of Wisconsin–Madison. He is an active member of the American Statistical Association and was chair of its section on Statistical Consulting in 2009. He also is an active member of the Eastern North American Region of the International Biometrics Society and aws elected by the voting membership to the Regional Committee from 2003 to 2006. Professor Craig has served on the editorial board of several statistical journals and has been a member of several data and safety monitoring boards, including Purdue's IRB. Professor Craig's research interests focus on the development of novel statistical methodology to address research questions in the life sciences. Areas of current interest are protein structure determination, diagnostic testing, and animal abundance estimation. In 2005, he was named Purdue University Faculty Scholar.

To Teachers: About This Book

To Students: What Is Statistics?

About the Authors

Data Table Index

Beyond the Basics Index

**Part I Looking at Data**CHAPTER 1

Introduction

1.1 Data

Section 1.1 Summary

Section 1.1 Exercises

Quantitative variables: stemplots and histograms

Histograms

Data analysis in action: Don’t hang up on me

Examining distributions

Dealing with outliers

Time plots

Section 1.2 Summary

Section 1.2 Exercises

Measuring center: the median

Mean versus median

Measuring spread: the quartiles

The five-number summary and boxplots

The 1.5 × IQR rule for suspected outliers

Measuring spread: the standard deviation

Properties of the standard deviation

Choosing measures of center and spread

Changing the unit of measurement

Section 1.3 Summary

Section 1.3 Exercises

Measuring center and spread for density curves

Normal distributions

The 68–95–99.7 rule

Standardizing observations

Normal distribution calculations

Using the standard Normal table

Inverse Normal calculations

Normal quantile plots

Beyond the Basics: Density estimation

Section 1.4 Summary

Section 1.4 Exercises

Chapter 1 Exercises

CHAPTER 2**Looking at Data—Relationships Introduction 2.1 Relationships **Examining relationships

Section 2.1 Summary

Section 2.1 Exercises

The log transformation

Adding categorical variables to scatterplots

Beyond the Basics: Scatterplot smoothers

Categorical explanatory variables

Section 2.2 Summary

Section 2.2 Exercises

Properties of correlation

Section 2.3 Summary

Section 2.3 Exercises

Prediction

Least-squares regression

Interpreting the regression line

Facts about least-squares regression

Correlation and regression

Another view of r2

Section 2.4 Summary

Section 2.4 Exercises

Outliers and influential observations

Beware of the lurking variable

Beware of correlations based on averaged data

Beware of restricted ranges

Beyond the Basics: Data mining

Section 2.5 Summary

Section 2.5 Exercises

Joint distribution

Marginal distributions

Describing relations in two-way tables

Conditional distributions

Simpson’s paradox

Section 2.6 Summary

Section 2.6 Exercises

Establishing causation

Section 2.7 Summary

Section 2.7 Exercises

Chapter 2 Exercises

CHAPTER 3**Producing Data Introduction 3.1 Sources of Data **Anecdotal data

Available data

Sample surveys and experiments

Section 3.1 Summary

Section 3.1 Exercises

Randomization

Randomized comparative experiments

How to randomize

Randomization using software

Randomization using random digits

Cautions about experimentation

Matched pairs designs

Block designs

Section 3.2 Summary

Section 3.2 Exercises

Selection of a simple random sample using software

Selection of a simple random sample using random digits

Stratified random samples

Multistage random samples

Cautions about sample surveys

Beyond the Basics: Capture-recapture sampling

Section 3.3 Summary

Section 3.3 Exercises

Informed consent

Confidentiality

Clinical trials

Behavioral and social science experiments

Section 3.4 Summary

Section 3.4 Exercises

Chapter 3 Exercises

**Part II Probability and Inference**CHAPTER 4

Introduction

4.1 Randomness

Thinking about randomness

The uses of probability

Section 4.1 Summary

Section 4.1 Exercises

Probability rules

Assigning probabilities: finite number of outcomes

Assigning probabilities: equally likely outcomes

Independence and the multiplication rule

Applying the probability rules

Section 4.2 Summary

Section 4.2 Exercises

Continuous random variables

Normal distributions as probability distributions

Section 4.3 Summary

Section 4.3 Exercises

Statistical estimation and the law of large numbers

Thinking about the law of large numbers

Beyond the Basics: More laws of large numbers

Rules for means

The variance of a random variable

Rules for variances and standard deviations

Section 4.4 Summary

Section 4.4 Exercises

Conditional probability

General multiplication rules

Tree diagrams

Bayes’s rule

Independence again

Section 4.5 Summary

Section 4.5 Exercises

Chapter 4 Exercises

CHAPTER 5**Sampling Distributions Introduction 5.1 Toward Statistical Inference **Sampling variability

Sampling distributions

Bias and variability

Sampling from large populations

Why randomize?

Section 5.1 Summary

Section 5.1 Exercises

The central limit theorem

A few more facts

Beyond the Basics: Weibull distributions

Section 5.2 Summary

Section 5.2 Exercises

Binomial distributions in statistical sampling

Finding binomial probabilities

Binomial mean and standard deviation

Sample proportions

Normal approximation for counts and proportions

The continuity correction

Binomial formula

The Poisson distributions

Section 5.3 Summary

Section 5.3 Exercises

Chapter 5 Exercises

CHAPTER 6**Introduction to Inference Introduction **Overview of inference

Confidence intervals

Confidence interval for a population mean

How confidence intervals behave

Choosing the sample size

Some cautions

Section 6.1 Summary

Section 6.1 Exercises

Stating hypotheses

Test statistics

P-values

Statistical significance

Tests for a population mean

Two-sided significance tests and confidence intervals

The P-value versus a statement of significance

Section 6.2 Summary

Section 6.2 Exercises

What statistical significance does not mean

Don’t ignore lack of significance

Statistical inference is not valid for all sets of data

Beware of searching for significance

Section 6.3 Summary

Section 6.3 Exercises

Increasing the power

Inference as decision

Two types of error

Error probabilities

The common practice of testing hypotheses

Section 6.4 Summary

Section 6.4 Exercises

Chapter 6 Exercises

CHAPTER 7**Inference for Means Introduction 7.1 Inference for the Mean of a Population **The t distributions

The one-sample t confidence interval

The one-sample t test

Matched pairs t procedures

Robustness of the t procedures

Beyond the Basics: The bootstrap

Section 7.1 Summary

Section 7.1 Exercises

The two-sample t procedures

The two-sample t confidence interval

The two-sample t significance test

Robustness of the two-sample procedures

Inference for small samples

Software approximation for the degrees of freedom

The pooled two-sample t procedures

Section 7.2 Summary

Section 7.2 Exercises

Inference for non-Normal populations

Transforming data

Use of a distribution-free procedure

Section 7.3 Summary

Section 7.3 Exercises

Chapter 7 Exercises

CHAPTER 8**Inference for Proportions Introduction 8.1 Inference for a Single Proportion **Large-sample confidence interval for a single proportion

Beyond the Basics: The plus four confidence interval for a single proportion

Significance test for a single proportion

Choosing a sample size for a confidence interval

Choosing a sample size for a significance test

Section 8.1 Summary

Section 8.1 Exercises

Beyond the Basics: The plus four confidence interval for a difference in proportions

Significance test for a difference in proportions

Choosing a sample size for two sample proportions

Beyond the Basics: Relative risk

Section 8.2 Summary

Section 8.2 Exercises

Chapter 8 Exercises

**Part III Topics in Inference**CHAPTER 9

Introduction

The hypothesis: no association

Expected cell counts

The chi-square test

Computations

Computing conditional distributions

The chi-square test and the z test

Beyond the Basics: Meta-analysis

Section 9.1 Summary

Section 9.1 Exercises

9.2 Goodness of Fit

Section 9.2 Summary

Section 9.2 Exercises

Chapter 9 Exercises

CHAPTER 10**Inference for Regression Introduction 10.1 Simple Linear Regression **Statistical model for linear regression

Preliminary data analysis and inference considerations

Estimating the regression parameters

Checking model assumptions

Confidence intervals and significance tests

Confidence intervals for mean response

Prediction intervals

Transforming variables

Beyond the Basics: Nonlinear regression

Section 10.1 Summary

Section 10.1 Exercises

The ANOVA F test

Calculations for regression inference

Inference for correlation

Section 10.2 Summary

Section 10.2 Exercises

Chapter 10 Exercises

CHAPTER 11**Multiple Regression Introduction 11.1 Inference for Multiple Regression **Population multiple regression equation

Data for multiple regression

Multiple linear regression model

Estimation of the multiple regression parameters

Confidence intervals and significance tests for regression coefficients

ANOVA table for multiple regression

Squared multiple correlation R2

Section 11.1 Summary

Section 11.1 Exercises

Relationships between pairs of variables

Regression on high school grades

Interpretation of results

Examining the residuals

Refining the model

Regression on SAT scores

Regression using all variables

Test for a collection of regression coefficients

Beyond the Basics: Multiple logistic regression

Section 11.2 Summary

Section 11.2 Exercises

Chapter 11 Exercises

CHAPTER 12**One-Way Analysis of Variance Introduction 12.1 Inference for One-Way Analysis of Variance **Data for one-way ANOVA

Comparing means

The two-sample t statistic

An overview of ANOVA

The ANOVA model

Estimates of population parameters

Testing hypotheses in one-way ANOVA

The ANOVA table

The F test

Software

Beyond the Basics: Testing the Equality of Spread

Section 12.1 Summary

Section 12.1 Exercises

Multiple comparisons

Power

Section 12.2 Summary

Section 12.2 Exercises

Chapter 12 Exercises

CHAPTER 13**Two-Way Analysis of Variance Introduction 13.1 The Two-Way ANOVA Model **Advantages of two-way ANOVA

The two-way ANOVA model

Main effects and interactions

Chapter 13 Summary

Chapter 13 Exercises

**Companion Chapters**(on the IPS website www.macmillanhighered.com/ips9e and in LaunchPad)

CHAPTER 14

Introduction

14.1 The Logistic Regression Model

Odds for two groups

Model for logistic regression

Fitting and interpreting the logistic regression model

Multiple logistic regression

Chapter 14 Summary

Chapter 14 Exercises

Chapter 14 Notes and Data Sources

CHAPTER 15**Nonparametric Tests Introduction 15.1 The Wilcoxon Rank Sum Test **The rank transformation

The Wilcoxon rank sum test

The Normal approximation

What hypotheses does Wilcoxon test?

Ties

Rank, t, and permutation tests

Section 15.1 Summary

Section 15.1 Exercises

Ties

Testing a hypothesis about the median of a distribution

Section 15.2 Summary

Section 15.2 Exercises

The Kruskal-Wallis test

Section 15.3 Summary

Section 15.3 Exercises

Chapter 15 Exercises

Chapter 15 Notes and Data Sources

CHAPTER 16**Bootstrap Methods and Permutation Tests Introduction **Software

Thinking about the bootstrap idea

Using software

Section 16.1 Summary

Section 16.1 Exercises

Bootstrapping to compare two groups

Beyond the Basics: The bootstrap for a scatterplot smoother

Section 16.2 Summary

Section 16.2 Exercises

Bootstrapping a sample median

Section 16.3 Summary

Section 16.3 Exercises

A more accurate bootstrap confidence interval: BCa

Confidence intervals for the correlation

Section 16.4 Summary

Section 16.4 Exercises

Permutation tests in practice

Permutation tests in other settings

Section 16.5 Summary

Section 16.5 Exercises

Chapter 16 Exercises

Chapter 16 Notes and Data Sources

CHAPTER 17**Statistics for Quality: Control and Capability Introduction **Use of data to assess quality

Statistical process control

x ̅ charts for process monitoring

s charts for process monitoring

Section 17.1 Summary

Section 17.1 Exercises

Additional out-of-control rules

Setting up control charts

Comments on statistical control

Don’t confuse control with capability!

Section 17.2 Summary

Section 17.2 Exercises

Cautions about capability indexes

Section 17.3 Summary

Section 17.3 Exercises

Section 17.4 Summary

Section 17.4 Exercises

Chapter 17 Exercises

Chapter 17 Notes and Data Sources

Tables

Answers to Odd-Numbered Exercises

Notes and Data Sources

Index