## Stats: Data and Models, Global Edition

(2016)

### Abstract

Richard De Veaux, Paul Velleman, and David Bock wrote Stats: Data and Models with the goal that students and instructors have as much fun reading it as they did writing it. Maintaining a conversational, humorous, and informal writing style, this new edition engages students from the first page. The authors focus on statistical thinking throughout the text and rely on technology for calculations. As a result, students can focus on developing their conceptual understanding. Innovative Think/Show/Tell examples give students a problem-solving framework and, more importantly, a way to think through any statistics problem and present their results. The Fourth Edition is updated with instructor podcasts, video lectures, and new examples to keep material fresh, current, and relevant to today’s students.

MyStatLab not included. Students, if MyStatLab is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN and course ID. MyStatLab should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information.

MyStatLab is an online homework, tutorial, and assessment product designed to personalize learning and improve results. With a wide range of interactive, engaging, and assignable activities, students are encouraged to actively learn and retain tough course concepts.

Section Title Page Action Price
Cover Cover
Title Page 1
Preface 9
Supplements 13
Acknowledgments 16
Part I Exploring and Understanding Data 17
Chapter 1 Stats Starts Here 17
1.1 What Is Statistics? 17
1.2 Data 19
1.3 Variables 21
Chapter 2 Displaying and Describing Categorical Data 32
2.1 Summarizing and Displaying a Single Categorical Variable 33
2.2 Exploring the Relationship Between Two Categorical Variables 36
Chapter 3 Displaying and Summarizing Quantitative Data 61
3.1 Displaying Quantitative Variables 61
3.2 Shape 66
3.3 Center 69
3.5 Boxplots and 5-Number Summaries 72
3.6 The Center of Symmetric Distributions: The Mean 75
3.7 The Spread of Symmetric Distributions: The Standard Deviation 77
3.8 Summary—What to Tell About a Quantitative Variable 79
Chapter 4 Understanding and Comparing Distributions 100
4.1 Comparing Groups with Histograms 101
4.2 Comparing Groups with Boxplots 102
4.3 Outliers 105
4.5 Re-Expressing Data: A First Look 110
Chapter 5 The Standard Deviation as a Ruler and the Normal Model 128
5.1 Standardizing with z-Scores 129
5.2 Shifting and Scaling 131
5.3 Normal Models 135
5.4 Finding Normal Percentiles 139
5.5 Normal Probability Plots 145
Part II Exploring Relationships Between Variables 167
Chapter 6 Scatterplots, Association, and Correlation 167
6.1 Scatterplots 168
6.2 Correlation 171
6.3 Warning: Correlation fi Causation 179
*6.4 Straightening Scatterplots 181
Chapter 7 Linear Regression 198
7.1 Least Squares: The Line of “Best Fit” 199
7.2 The Linear Model 200
7.3 Finding the Least Squares Line 201
7.4 Regression to the Mean 205
7.5 Examining the Residuals 208
7.6 R2—The Variation Accounted For by the Model 210
7.7 Regression Assumptions and Conditions 212
Chapter 8 Regression Wisdom 235
8.1 Examining Residuals 235
8.2 Extrapolation: Reaching Beyond the Data 238
8.3 Outliers, Leverage, and Influence 242
8.4 Lurking Variables and Causation 245
8.5 Working with Summary Values 246
Chapter 9 Re-expressing Data: Get It Straight! 263
9.1 Straightening Scatterplots – The Four Goals 264
9.2 Finding a Good Re-Expressio 268
Part III Gathering Data 296
Chapter 10 Understanding Randomness 296
10.1 What Is Randomness? 297
10.2 Simulating by Hand 298
Chapter 11 Sample Surveys 310
11.1 The Three Big Ideas of Sampling 311
11.2 Populations and Parameters 314
11.3 Simple Random Samples 315
11.4 Other Sampling Designs 316
11.5 From the Population to the Sample: You Can’t Always Get What You Want 321
11.6 The Valid Survey 322
11.7 Common Sampling Mistakes, or How to Sample Badly 323
Chapter 12 Experiments and Observational Studies 334
12.1 Observational Studies 334
12.2 Randomized, Comparative Experiments 335
12.3 The Four Principles of Experimental Design 337
12.4 Control Treatments 342
12.5 Blocking 345
12.6 Confounding 346
Part IV Randomness and Probability 364
Chapter 13 From Randomness to Probability 364
13.1 Random Phenomena 364
13.2 Modeling Probability 367
13.3 Formal Probability 369
Chapter 14 Probability Rules! 382
14.1 The General Addition Rule 382
14.2 Conditional Probability and the General Multiplication Rule 387
14.3 Independence 389
14.4 Picturing Probability: Tables, Venn Diagrams, and Trees 391
14.5 Reversing the Conditioning and Bayes’ Rule 394
Chapter 15 Random Variables 405
15.1 Center: The Expected Value 405
15.2 Spread: The Standard Deviation 407
15.3 Shifting and Combining Random Variables 410
15.4 Continuous Random Variables 416
Chapter 16 Probability Models 428
16.1 Bernoulli Trials 428
16.2 The Geometric Model 429
16.3 The Binomial Model 432
16.4 Approximating the Binomial with a Normal Model 435
16.5 The Continuity Correction 437
16.6 The Poisson Model 439
16.7 Other Continuous Random Variables: The Uniform and the Exponential 441
Part V From the Data at Hand to the World at Large 459
Chapter 17 Sampling Distribution Models 459
17.1 Sampling Distribution of a Proportion 459
17.2 When Does the Normal Model Work? Assumptions and Conditions 463
17.3 The Sampling Distribution of Other Statistics 467
17.4 The Central Limit Theorem: The Fundamental Theorem of Statistics 468
17.5 Sampling Distributions: A Summary 474
Chapter 18 Confidence Intervals for Proportions 488
18.1 A Confidence Interval 489
18.2 Interpreting Confidence Intervals: What Does 95% Confidence Really Mean? 491
18.3 Margin of Error: Certainty vs. Precision 493
18.4 Assumptions and Conditions 495
Chapter 19 Testing Hypotheses About Proportions 510
19.1 Hypotheses 511
19.2 P-Values 512
19.3 The Reasoning of Hypothesis Testing 514
19.4 Alternative Alternatives 517
19.5 P-Values and Decisions: What to Tell About a Hypothesis Test 520
Chapter 20 Inferences About Means 534
20.1 Getting Started: The Central Limit Theorem (Again) 535
20.2 Gosset’s t 536
20.3 Interpreting Confidence Intervals 545
20.4 A Hypothesis Test for the Mean 545
20.5 Choosing the Sample Size 550
Chapter 21 More About Tests and Intervals 564
21.1 Choosing Hypotheses 564
21.2 How to Think About P-Values 566
21.3 Alpha Levels 570
21.4 Critical Values for Hypothesis Tests 573
21.5 Errors 576
Part VI Accessing Associations Between Variables 601
Chapter 22 Comparing Groups 601
22.1 The Standard Deviation of a Difference 602
22.2 Assumptions and Conditions for Comparing Proportions 604
22.3 A Confidence Interval for the Difference Between Two Proportions 605
22.4 The Two Sample z-Test: Testing for the Difference Between Proportions 608
22.5 A Confidence Interval for the Difference Between Two Means 612
22.6 The Two-Sample t-Test: Testing for the Difference Between Two Means 616
22.7 The Pooled t-Test: Everyone into the Pool? 622
Chapter 23 Paired Samples and Blocks 646
23.1 Paired Data 647
23.2 Assumptions and Conditions 648
23.3 Confidence Intervals for Matched Pairs 653
23.4 Blocking 656
Chapter 24 Comparing Counts 671
24.1 Goodness-of-Fit Tests 671
24.2 Chi-Square Test of Homogeneity 679
24.3 Examining the Residuals 683
24.4 Chi-Square Test of Independence 685
Chapter 25 Inferences for Regression 705
25.1 The Population and the Sample 706
25.2 Assumptions and Conditions 707
25.3 Intuition About Regression Inference 712
25.4 Regression Inference 715
25.5 Standard Errors for Predicted Values 721
25.6 Confidence Intervals for Predicted Values 722
25.7 Logistic Regression 724
Part VII Inference When Variables Are Related 763
Chapter *26 Analysis of Variance 763
26.1 Testing Whether the Means of Several Groups Are Equal 765
26.2 The ANOVA Table 769
26.3 Assumptions and Conditions 775
26.4 Comparing Means 781
26.5 ANOVA on Observational Data 783
Chapter 27 Multifactor Analysis of Variance 798
27.1 A Two Factor ANOVA Model 799
27.2 Assumptions and Conditions 800
27.3 Interactions 812
Chapter 28 Multiple Regression 833
28.1 What Is Multiple Regression? 833
28.2 Interpreting Multiple Regression Coefficients 835
28.3 The Multiple Regression Model—Assumptions and Conditions 837
28.4 Multiple Regression Inference 841
28.5 Comparing Multiple Regression Models 848
Chapter 29 Multiple Regression Wisdom (available online) 875
29.1 Indicators 877
29.2 Diagnosing Regression Models: Looking at the Cases 881
29.3 Building Multiple Regression Models 888
Appendixes A-1
B Photo Acknowledgments A-51
C Index A-53
A A-53
B A-53
C A-53
D A-55
E A-55
F A-56
G A-56
H A-56
I A-56
J A-56
K A-57
L A-57
M A-57
N A-57
O A-58
P A-58
Q A-59
R A-59
S A-60
T A-61
U A-61
V A-61
W A-62
X A-62
Y A-62
Z A-62
D Tables and Selected Formulas A-69