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

By

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

Stats Data and Models, Third Canadian Edition

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

This book is US$10. Order for this book:
(Request for free sample pages click on "Order Now" button)

Book Order
Or, Send email: [email protected]

Share this Book!

Leave a Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.