Introductory statistics: exploring the world through data/ Robert Gould

CASE STUDY • Deadly Cell Phones?
1.1 What Are Data? 4
1.2 Classifying and Storing Data
1.3 Organizing Categorical Data
1.4 Collecting Data To Understand Causality 14
EXPLORING STATISTiCS > Collecting a Table of Different Kinds of Data 24
Picturing Variation with Graphs 34
CASE STUDY • Student-to-Teacher Ratio at Private and Public Colleges 35
2.1 Visualizing Variation in Numerical Data 36
2.2 Summarizing Important Features ofa Numerical Distribution 4l
2.3 Visualizing Variation in Categorical Variables 49
2.4 Summarizing Categorical Distributions 52
2.5 Interpreting Graphs 55
EXPLORING STATISTICS > Personal Distance 58
Numerical Summaries of Center and Variation 76
CASE STUDY • Living in a Risky World 77
3.1 Summaries for Symmetric Distributions 78
3.2 What's Unusual? The Empirical Rule and z-Scores 88
3.3 Summaries for Skewed Distributions 93
3.4 Comparing Measures of Center 100
3.5 Using Boxplots for Displaying Summaries 104
EXPLORING STATISTICS > Does Reaction Distance Depend on Gender? 110
Regression Analysis: Exploring Associations
between Variables 132
CASE STUDY • Catching Meter Thieves 133
4.1 Visualizing Variability with a Scatterplot 134
4.2 Measuring Strength of Association with Correlation 138
4.3 Modeling Linear Trends 146
4.4 Evaluating the Linear Model 161
EXPLORING STATISTICS Guessing the Age of Famous People
CASE STUDY • SIDS or Murder? 195
5.1 What is Randomness? 196
5.2 Finding Theoretical Probabilities 200
5.3 Associations in Categorical Variables 209
5.4 Finding Empirical Probabilities with
Simulations 220
EXPLORING STATISTICS > "Let's Make a Deal": Stay or Switch? 228
Modeling Random Events: The Normal and
Binomial Models 242
CASE STUDY • You Sometimes Get More Than You Pay For 243
6.1 Probability Distributions Are Models of Random
Experiments 244
6.2 The Normal Model 249
6.3 The Binomial Model 262
EXPLORING STATISTICS > ESP with Coin Flipping 277
CHAPTER y? Survey Sampling and Inference 294
CHAPTER
CASE STUDY • Spring Break Fever: Just What the Doctors Ordered? 295
7.1 Learning about the World through Surveys 296
7.2 Measuring the Quality of a Survey 302
7.3 The Central Limit Theorem for Sample Proportions 311
7.4 Estimating the Population Proportion with Confidence
Intervals 317
7.5 Margin of Error and Sample Size for Proportions 323
EXPLORING STATISTICS Simple Random Sampling Prevents Bias 325
Hypothesis Testing for Population Proportions 338
CASE STUDY • Violence on TV 339
8.1 The Main Ingredients of Hypothesis Testing 340
8.2 Characterizing p-values 346
8.3 Hypothesis Testing in Four Steps 349
8.4 Comparing Proportions from Two Populations 358
8.5 Understanding Hypothesis Testing 363
EXPLORING STATISTICS > Identifying Flavors of Gum through Smell 370
CHAPTER
Inferring Population Means 386
CASE STUDY • Epilepsy Drugs and Children 387
9.1 Sample Means of Random Samples 388
9.2 The Central Limit Theorem for Sample Means 392
9.3 Answering Questions about the Mean ofa Population
9.4 Comparing Two Population Means 411
9.5 Overview of Analyzing Means 421
EXPLORING STATISTICS !> Pulse Rates 425
CHAPTER U) Associations between Categorical Variables 448
CASE STUDY • Popping Better Popcorn 449
10.1 The Basic Ingredients for Testing with Categorical Variables
10.2 The Chi-Square Test for Goodness of Fit 460
CONTENTS
10.3 Chi-Square Tests for Associations between Categorical Variables
10.4 Hypothesis Tests When Sample Sizes Are Small 473
EXPLORING STATISTICS > Skittles 479
Multiple Comparisons and Analysis of Variance 498
CASE STUDY • Seeing Red 499
11.1 Multiple Comparisons 500
11.2 The Analysis of Variance 506
11.3 The ANOVA Test 513
11.4 Post-Hoc Procedures 518
EXPLORING STATISTICS > Recovery Heart Rate 525
Experimental Design: Controlling Variation 540
CASE STUDY • Does Stretching Improve Athletic Performance? 541
12.1 Variation Out of Control 542
12.2 Controlling Variation in Surveys 550
12.3 Reading Research Papers 555
EXPLORING STATISTICS > Reporting on Research Articles 563
Inference without NormaLIty 574
CASE STUDY • Electric Rays 575
13.1 Transforming Data 576
13.2 The Sign Test for Paired Data 584

XII CONTENTS
13.3 Mann-Whitney Test for Two Independent Groups 588
13.4 Randomization Tests 593
EXPLORING STATISTICS • Balancing on One Foot 600
CHAPTER Inference for Regression 622
CASE STUDY • Building a Better Oyster Shucker 623
14.1 The Linear Regression Model 624
14.2 Using the Linear Model 633
14.3 Predicting Values and Estimating Means 641
EXPLORING STATISTICS >• Older and Slower? 650