# Stats: Data and Models, 3rd Canadian Edition PDF by Richard D. De Veaux, Paul F. Velleman, David E. Bock, Augustin M. Vukov And Augustine C.M. Wong

## 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

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

17.1 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

18.1 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

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