Ninth Edition   ©2017

Introduction to the Practice of Statistics

David S. Moore (Purdue University) , George P. McCabe (Purdue University) , Bruce A. Craig (Purdue University)

  • ISBN-10: 1-319-01338-4; ISBN-13: 978-1-319-01338-7; Format: Cloth Text, 814 pages

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
Looking at Data—Distributions

1.1 Data
Key characteristics of a data set
Section 1.1 Summary
Section 1.1 Exercises

1.2 Displaying Distributions with Graphs
Categorical variables: bar graphs and pie charts
Quantitative variables: stemplots and 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

1.3 Describing Distributions with Numbers
Measuring center: the mean
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

1.4 Density Curves and Normal Distributions
Density curves
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

Looking at Data—Relationships
2.1 Relationships
Examining relationships
Section 2.1 Summary
Section 2.1 Exercises

2.2 Scatterplots
Interpreting scatterplots
The log transformation
Adding categorical variables to scatterplots
Beyond the Basics: Scatterplot smoothers
Categorical explanatory variables
Section 2.2 Summary
Section 2.2 Exercises

2.3 Correlation
The correlation r
Properties of correlation
Section 2.3 Summary
Section 2.3 Exercises

2.4 Least-Squares Regression
Fitting a Line to Data
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

2.5 Cautions about Correlation and Regression
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

2.6 Data Analysis for Two-Way Tables
The two-way table
Joint distribution
Marginal distributions
Describing relations in two-way tables
Conditional distributions
Simpson’s paradox
Section 2.6 Summary
Section 2.6 Exercises

2.7 The Question of Causation
Explaining association
Establishing causation
Section 2.7 Summary
Section 2.7 Exercises
Chapter 2 Exercises

Producing Data
3.1 Sources of Data
Anecdotal data
Available data
Sample surveys and experiments
Section 3.1 Summary
Section 3.1 Exercises

3.2 Design of Experiments
Comparative experiments
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

3.3 Sampling Design
Simple random samples
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

3.4 Ethics
Institutional review boards
Informed consent
Clinical trials
Behavioral and social science experiments
Section 3.4 Summary
Section 3.4 Exercises
Chapter 3 Exercises

Part II Probability and Inference
Probability: The Study of Randomness
4.1 Randomness
The language of probability
Thinking about randomness
The uses of probability
Section 4.1 Summary
Section 4.1 Exercises

4.2 Probability Models
Sample spaces
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

4.3 Random Variables
Discrete random variables
Continuous random variables
Normal distributions as probability distributions
Section 4.3 Summary
Section 4.3 Exercises

4.4 Means and Variances of Random Variables
The mean of a random variable
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

4.5 General Probability Rules
General addition rules
Conditional probability
General multiplication rules
Tree diagrams
Bayes’s rule
Independence again
Section 4.5 Summary
Section 4.5 Exercises
Chapter 4 Exercises

Sampling Distributions
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

5.2 The Sampling Distribution of a Sample Mean
The mean and standard deviation of x ̅ 
The central limit theorem
A few more facts
Beyond the Basics: Weibull distributions
Section 5.2 Summary
Section 5.2 Exercises

5.3 Sampling Distributions for Counts and Proportions
The binomial distributions for sample counts
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

Introduction to Inference
Overview of inference
6.1 Estimating with Confidence
Statistical confidence
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

6.2 Tests of Significance
The reasoning of significance tests
Stating hypotheses
Test statistics
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

6.3 Use and Abuse of Tests
Choosing a level of significance
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

6.4 Power and Inference as a Decision
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

Inference for Means
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

7.2 Comparing Two Means
The two-sample z statistic
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

7.3 Additional Topics on Inference
Choosing the sample size
Inference for non-Normal populations
Transforming data
Use of a distribution-free procedure
Section 7.3 Summary
Section 7.3 Exercises
Chapter 7 Exercises

Inference for Proportions
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

8.2 Comparing Two Proportions
Large-sample confidence interval for a difference in proportions
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

Analysis of Two-Way Tables
9.1 Inference for Two-Way Tables
The hypothesis: no association
Expected cell counts
The chi-square test
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

Inference for Regression
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

10.2 More Detail about Simple Linear Regression
Analysis of variance for regression
The ANOVA F test
Calculations for regression inference
Inference for correlation
Section 10.2 Summary
Section 10.2 Exercises
Chapter 10 Exercises

Multiple Regression
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
11.2 A Case Study
Preliminary analysis
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

One-Way Analysis of Variance
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
Beyond the Basics: Testing the Equality of Spread
Section 12.1 Summary
Section 12.1 Exercises

12.2 Comparing the Means
Multiple comparisons
Section 12.2 Summary
Section 12.2 Exercises
Chapter 12 Exercises

Two-Way Analysis of Variance
13.1 The Two-Way ANOVA Model
Advantages of two-way ANOVA
The two-way ANOVA model
Main effects and interactions
13.2 Inference for Two-Way ANOVA
The ANOVA table for two-way ANOVA
Chapter 13 Summary
Chapter 13 Exercises

Companion Chapters
(on the IPS website and in LaunchPad)
Logistic Regression
14.1 The Logistic Regression Model
Binomial distributions and odds
Odds for two groups
Model for logistic regression
Fitting and interpreting the logistic regression model

14.2 Inference for Logistic Regression
Confidence intervals and significance tests
Multiple logistic regression
Chapter 14 Summary
Chapter 14 Exercises
Chapter 14 Notes and Data Sources

Nonparametric Tests
15.1 The Wilcoxon Rank Sum Test
The rank transformation
The Wilcoxon rank sum test
The Normal approximation
What hypotheses does Wilcoxon test?
Rank, t, and permutation tests
Section 15.1 Summary
Section 15.1 Exercises

15.2 The Wilcoxon Signed Rank Test
The Normal approximation
Testing a hypothesis about the median of a distribution
Section 15.2 Summary
Section 15.2 Exercises

15.3 The Kruskal-Wallis Test
Hypotheses and assumptions
The Kruskal-Wallis test
Section 15.3 Summary
Section 15.3 Exercises
Chapter 15 Exercises
Chapter 15 Notes and Data Sources

Bootstrap Methods and Permutation Tests
16.1 The Bootstrap Idea
The big idea: resampling and the bootstrap distribution
Thinking about the bootstrap idea
Using software
Section 16.1 Summary
Section 16.1 Exercises

16.2 First Steps in Using the Bootstrap
Bootstrap t confidence intervals
Bootstrapping to compare two groups
Beyond the Basics: The bootstrap for a scatterplot smoother
Section 16.2 Summary
Section 16.2 Exercises

16.3 How Accurate Is a Bootstrap Distribution?
Bootstrapping small samples
Bootstrapping a sample median
Section 16.3 Summary
Section 16.3 Exercises

16.4 Bootstrap Confidence Intervals
Bootstrap percentile confidence intervals
A more accurate bootstrap confidence interval: BCa
Confidence intervals for the correlation
Section 16.4 Summary
Section 16.4 Exercises

16.5 Significance Testing Using Permutation Tests
Using software
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

Statistics for Quality: Control and Capability
Use of data to assess quality
17.1 Processes and Statistical Process Control
Describing processes
Statistical process control
x ̅ charts for process monitoring
s charts for process monitoring
Section 17.1 Summary
Section 17.1 Exercises

17.2 Using Control Charts
x ̅ and R charts
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

17.3 Process Capability Indexes
The capability indexes Cp and Cpk
Cautions about capability indexes
Section 17.3 Summary
Section 17.3 Exercises

17.4 Control Charts for Sample Proportions
Control limits for p charts
Section 17.4 Summary
Section 17.4 Exercises
Chapter 17 Exercises
Chapter 17 Notes and Data Sources

Answers to Odd-Numbered Exercises 
Notes and Data Sources