Stats: Data and Models, Third Canadian Edition
By Richard D. De Veaux, Paul F. Velleman, David E. Bock, Augustin M. Vukov And Augustine C.M. Wong
Contents:
Preface xiii
Part I Exploring and Understanding Data
1 Stats Starts Here 1
1.1 What Is Statistics? ■ 1.2 Data ■ 1.3 Variables
2 Displaying and Describing Categorical Data 17
2.1 Summarizing and Displaying a Single Categorical Variable
■ 2.2 Exploring the Relationship Between Two Categorical Variables
3 Displaying and Summarizing Quantitative Data 50
3.1 Displays for Quantitative Variables ■ 3.2 Shape ■ 3.3 Centre
■ 3.4 Spread ■ 3.5 Boxplots and 5-Number Summaries ■ 3.6 The Centre of
Symmetric Distributions: The Mean ■ 3.7 The Spread of Symmetric
Distributions: The Standard Deviation ■ 3.8 Summary—What to Tell
About a Quantitative Variable
4 Understanding and Comparing Distributions 98
4.1 Comparing Groups ■ 4.2 Comparing Boxplots ■ 4.3 Outliers
■ 4.4 Timeplots: Order, Please! ■ 4.5 Re-expressing Data: A First Look
5 The Standard Deviation as a Ruler and the Normal Model 131
5.1 Standardizing with z-Scores ■ 5.2 Shifting and Scaling ■ 5.3 Density
Curves and the Normal Model ■ 5.4 Finding Normal Percentiles
■ 5.5 Normal Probability Plots
Part I Review: Exploring and Understanding Data 165
Part II Exploring Relationships Between Variables
6 Scatterplots, Association, and Correlation 177
6.1 Scatterplots ■ 6.2 Correlation ■ 6.3 Warning:
Correlation Z Causation ■ *6.4 Straightening Scatterplots
7 Linear Regression 210
7.1 Least Squares: The Line of “Best Fit” ■ 7.2 The Linear Model
■ 7.3 Finding the Least Squares Line ■ 7.4 Regression to the Mean
■ 7.5 Examining the Residuals ■ 7.6 R2—The Variation Accounted
For by the Model ■ 7.7 Regression Assumptions and Conditions
8 Regression Wisdom 246
8.1 Examining Residuals ■ 8.2 Outliers, Leverage, and Influence
■ 8.3 Extrapolation: Reaching Beyond the Data ■ 8.4 Cautions
Part II Review Exploring Relationships Between Variables 280
Part III Gathering Data
9 Sample Surveys 290
9.1 The Three Big Ideas of Sampling ■ 9.2 Populations and
Parameters ■ 9.3 Simple Random Samples ■ 9.4 Other Sampling
Designs ■ 9.5 From the Population to the Sample: You Can’t Always
Get What You Want ■ 9.6 The Valid Survey ■ 9.7 Common Sampling
Mistakes, or How to Sample Badly
10 Experiments and Observational Studies 319
10.1 Observational Studies ■ 10.2 Randomized, Comparative
Experiments ■ 10.3 The Four Principles of Experimental Design
■ 10.4 Control Treatments ■ 10.5 Blocking ■ 10.6 Confounding
Part III Review: Gathering Data 348
Part IV Randomness and Probability
11 From Randomness to Probability 354
11.1 Random Phenomena ■ 11.2 Modelling Probability ■ 11.3 Formal
Probability Rules ■ 11.4 The General Addition Rule
12 Probability Rules! 374
12.1 Probability on Condition ■ 12.2 Independence and the
Multiplication Rule ■ 12.3 Picturing Probability: Tables,
Venn Diagrams, and Trees ■ 12.4 Reversing the Conditioning and Bayes’ Rule
13 Random Variables 399
13.1 Centre: The Expected Value or Mean ■ 13.2 Spread: The Variance and
Standard Deviation ■ 13.3 Combining Random Variables
■ 13.4 The Binomial Model ■ *13.5 The Poisson Model
■ 13.6 Continuous Models ■ 13.7 Approximating the
Binomial with a Normal Model ■ *13.8 The Continuity Correction
Part IV Review: Randomness and Probability 440
Part V From the Data at Hand to the World at Large
14 Sampling Distribution Models 446
14.1 Sampling Distribution of a Proportion ■ 14.2 When Does the
Normal Model Work? Assumptions and Conditions ■ 14.3 The Sampling
Distribution of Other Statistics ■ 14.4 The Central Limit Theorem: The
Fundamental Theorem of Statistics ■ 14.5 Sampling Distributions: A Summary
15 Confidence Intervals for Proportions 476
15.1 A Confidence Interval ■ 15.2 Interpreting Confidence Intervals: What
Does “95% Confidence” Really Mean? ■ 15.3 Margin of Error: Certainty
versus Precision ■ 15.4 Assumptions and Conditions ■ *15.5 The Plus Four
Confidence Interval for Small Samples ■ *15.6 Large Sample Confidence Intervals
16 Testing Hypotheses About Proportions 503
16.1 Hypotheses ■ 16.2 P-Values ■ 16.3 The Reasoning of Hypothesis
Testing ■ 16.4 Alternative Alternatives ■ 16.5 P-Values and Decisions: What
to Tell About a Hypothesis Test ■ *16.6 Large Sample Tests of Hypothesis
17 More About Tests 527
17.1 Choosing the Hypotheses ■ 17.2 How to Think About P-Values
■ 17.3 Alpha Levels and Significance ■ 17.4 Critical Values for
Hypothesis Tests ■ 17.5 Decision Errors ■ 17.6 Power and Sample Size
18 Inferences About Means 559
18.1 The Sampling Model for the Sample Mean ■ 18.2 Gosset’s t
■ 18.3 A t-Interval for the Mean ■ 18.4 Hypothesis Test for the Mean
■ 18.5 Determining the Sample Size
Part V Review: From the Data at Hand to the World at Large 595
Part VI Assessing Associations Between Variables
19 Comparing Means 602
19.1 Comparing Means of Independent Samples ■ 19.2 The Two-Sample
t-Test ■ 19.3 The Pooled t-Test ■ 19.4 Determining the Sample Size
20 Paired Samples and Blocks 637
20.1 Paired t-Test ■ 20.2 Assumptions and Conditions ■ 20.3 Paired t Confidence Interval ■ 20.4
Effect Size and Sample Size ■ 20.5 Blocking
■ 20.6 A Non-Parametric Alternative: The Sign Test
21 Comparing Two Proportions 666
21.1 The Standard Deviation of the Difference Between Two
Proportions ■ 21.2 Assumptions and Conditions When Comparing
Proportions ■ 21.3 A Confidence Interval for the Difference Between
Two Proportions ■ 21.4 The z-Test for a Difference Between Proportions
22 Comparing Counts 690
22.1 Goodness-of-Fit ■ 22.2 Chi-Square Test of Homogeneity
■ 22.3 Examining the Residuals ■ 22.4 Chi-Square Test of Independence
23 Inferences for Regression 725
23.1 A Regression Model ■ 23.2 Standard Errors of
Parameter Estimates ■ 23.3 Regression Inference
■ 23.4 Confidence and Prediction Intervals for the Response Variable
■ 23.5 Correlation Test ■ 23.6 The Analysis of Variance (ANOVA)
for Regression ■ 23.7 Logistic Regression
Part VI Review: Assessing Associations Between Variables 773
Part VII Modelling the World at Large
24 Analysis of Variance 787
24.1 Testing Whether the Means of Several Groups Are Equal
■ 24.2 The Analysis of Variance (ANOVA) ■ 24.3 Assumptions and Conditions
■ 24.4 Comparing Means ■ 24.5 ANOVA on Observational Data
25 Multifactor Analysis of Variance 826
25.1 A Two-Factor ANOVA Model ■ 25.2 Assumptions and
Conditions ■ 25.3 Adding Interaction to the Model
26 Multiple Regression 862
26.1 What is Multiple Regression? ■ 26.2 Interpreting Multiple Regression
Coefficients ■ 26.3 Model Assumptions and Conditions ■ 26.4 Multiple
Regression Inference ■ 26.5 Comparing Multiple Regression Models
27 Multiple Regression Wisdom 893
27.1 Indicator Variables ■ 27.2 Diagnosing Regression Models:
Looking at the Cases ■ 27.3 Building Multiple Regression Models
Part VII Review: Modelling the World at Large 930
Part VIII Distribution-free Methods
28 Nonparametric Tests 944
28.1 Wilcoxon Rank Sum Test ■ 28.2 Kruskal-Wallis Test ■ 28.3 Wilcoxon
Signed Rank Test for Paired Data ■ 28.4 Friedman Test for a Randomized
Block Design ■ 28.5 Rank Correlation
29 The Bootstrap 972
29 The Bootstrap (online only)
29.1 The Basic Idea ■ 29.2 Bootstrapping the Sampling Distribution
of a Sample Mean ■ 29.3 Bootstrapping the Standard Error
■ 29.4 Confidence Intervals ■ 29.5 Bootstrapping with the Median
■ 29.6 Bootstrap Assumptions and Conditions ■ 29.7 More Complicated
Data Structures
Appendixes
A Answers A-1 ■ B Index B-1 ■ C Tables C-1