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Table of Contents

Preface ix Unit A: Data 1 Chapter 1. Collecting Data 2 1.1. The Structure of Data 4 1.2. Sampling from a Population 16 1.3. Experiments and Observational Studies 29 Chapter 2. Describing Data 46 2.1. Categorical Variables 48 2.2. One Quantitative Variable: Shape and Center 63 2.3. One Quantitative Variable: Measures of Spread 77 2.4. Boxplots and Quantitative/Categorical Relationships 93 2.5. Two Quantitative Variables: Scatterplot and Correlation 106 2.6. Two Quantitative Variables: Linear Regression 123 2.7. Data Visualization and Multiple Variables 137 Unit A: Essential Synthesis 161 Review Exercises 174 Unit B: Understanding Inference 193 Chapter 3. Confidence Intervals 194 3.1. Sampling Distributions 196 3.2. Understanding and Interpreting Confidence Intervals 213 3.3. Constructing Bootstrap Confidence Intervals 228 3.4. Bootstrap Confidence Intervals using Percentiles 242 Chapter 4. Hypothesis Tests 256 4.1. Introducing Hypothesis Tests 258 4.2. Measuring Evidence with P-values 272 4.3. Determining Statistical Significance 288 4.4. A Closer Look at Testing 303 4.5. Making Connections 318 Unit B: Essential Synthesis 341 Review Exercises 351 Unit C: Inference with Normal and t-Distributions 369 Chapter 5. Approximating with a Distribution 370 5.1. Hypothesis Tests Using Normal Distributions 372 5.2. Confidence Intervals Using Normal Distributions 387 Chapter 6. Inference for Means and Proportions 402 6.1. Inference for a Proportion 6.1-D Distribution of a Proportion 404 6.1-CI Confidence Interval for a Proportion 407 6.1-HT Hypothesis Test for a Proportion 414 6.2. Inference for a Mean 6.2-D Distribution of a Mean 419 6.2-CI Confidence Interval for a Mean 424 6.2-HT Hypothesis Test for a Mean 433 6.3. Inference for a Difference in Proportions 6.3-D Distribution of a Difference in Proportions 438 6.3-CI Confidence Interval for a Difference in Proportions 441 6.3-HT Hypothesis Test for a Difference in Proportions 446 6.4. Inference for a Difference in Means 6.4-D Distribution of a Difference in Means 452 6.4-CI Confidence Interval for a Difference in Means 455 6.4-HT Hypothesis Test for a Difference in Means 461 6.5. Paired Difference in Means 468 Unit C: Essential Synthesis 477 Review Exercises 489 Unit D: Inference for Multiple Parameters 505 Chapter 7. Chi-Square Tests for Categorical Variables 506 7.1. Testing Goodness-of-Fit for a Single Categorical Variable 508 7.2. Testing for an Association between Two Categorical Variables 523 Chapter 8. ANOVA to Compare Means 538 8.1. Analysis of Variance 540 8.2. Pairwise Comparisons and Inference after ANOVA 563 Chapter 9. Inference for Regression 574 9.1. Inference for Slope and Correlation 576 9.2. ANOVA for Regression 591 9.3. Confidence and Prediction Intervals 603 Chapter 10. Multiple Regression 610 10.1. Multiple Predictors 612 10.2. Checking Conditions for a Regression Model 624 10.3. Using Multiple Regression 633 Unit D: Essential Synthesis 647 Review Exercises 661 The Big Picture: Essential Synthesis 669 Exercises for the Big Picture: Essential Synthesis 683 Chapter P. Probability Basics 688 P.1. Probability Rules 690 P.2. Tree Diagrams and Bayes' Rule 702 P.3. Random Variables and Probability Functions 709 P.4. Binomial Probabilities 716 P.5. Density Curves and the Normal Distribution 724 Appendix A. Chapter Summaries 737 Appendix B. Selected Dataset Descriptions 749 Partial Answers 761 Index General Index 783 Data Index 786

About the Author

Patti Frazer Lock is the Cummings Professor of Mathematics in the Department of Mathematics, Computer Science, and Statistics at St. Lawrence University. She is a member of the Calculus Consortium for Higher Education (formerly the Calculus Consortium based at Harvard). She is a co-author with the Consortium of texts in Calculus, Applied Calculus, Multivariable Calculus, Precalculus, and Algebra. She is currently working on a text in Introductory Statistics. She does workshops around the country on the teaching of undergraduate mathematics. She is a member of the Committee on the Undergraduate Program in Mathematics of the Mathematics Association of America, is on the Editorial Board of PRIMUS Journal, and is a Consultant to Project NExT of the MAA. She loves to teach and teaches courses across the spectrum of mathematics and statistics, and she enjoys collaborating with undergraduates on her research in graph theory. She received her BA from Colgate University and her Ph.D. from the University of Massachusetts at Amherst.

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