Statistics: Unlocking the Power of Data

by Robin H. Lock, Patti Frazer Lock, Kari Lock Morgan, Eric F. Lock, Dennis F. Lock

Overview

Statistics: Unlocking the Power of Data, 3^rd Edition is designed for an introductory statistics course focusing on data analysis with real-world applications.  Students use simulation methods to effectively collect, analyze, and interpret data to draw conclusions.  Randomization and bootstrap interval methods introduce the fundamentals of statistical inference, bringing concepts to life through authentically relevant examples. More traditional methods like t-tests, chi-square tests, etc. are introduced after students have developed a strong intuitive understanding of inference through randomization methods. While any popular statistical software package may be used, the authors have created StatKey to perform simulations using data sets and examples from the text.  A variety of videos, activities, and a modular chapter on probability are adaptable to many classroom formats and approaches.

Product Details

ISBN-13:	9781119682165
Publisher:	Wiley
Publication date:	10/13/2020
Edition description:	3rd ed.
Pages:	864
Product dimensions:	8.30(w) x 10.80(h) x 1.40(d)

About the Author

Robin H. Lock is Burry Professor of Statistics in the Department of Mathematics, Computer Science, and Statistics at St. Lawrence University. He is a Fellow of the American Statistical Association, past Chair of the Joint MAA-ASA Committee on Teaching Statistics, a member of the committee that developed GAISE (Guidelines for Assessment and Instruction in Statistics Education), and a member of the Consortium for the Advancement of Undergraduate Statistics Education, CAUSE. His work was recognized with the ASA's inaugural Waller Distinguished Teaching Career Awared in 2014 and he has won numerous other awards for presentations on statistics education at national conferences. He brings to the project an insider's understanding of national trends in statistics education.

Patti Frazer Lock is Cummings Professor of Mathematics at St. Lawrence University. She is a member of the Committee on the Undergraduate Program in Mathematics of the Mathematics Association of America, and chairs the MAA subcommittee writing the guidelines for the future of Intro Stats courses. She won the J. Calvin Keene faculty award at St. Lawrence University. She is a member of the Calculus Consortium and is a co-author on Hughes-Hallett's Calculus and Applied Calculus, Connally's Functions Modeling Change, and McCallum's Algebra and Multivariable Calculus texts. She is passionate about helping students succeed in, and enjoy, introductory courses in statistics and mathematics. She feels very lucky to be writing this book with her family!

Kari Lock Morgan is now an assistant professor in the Statistics Department at Penn State University after finishing her Ph. D. in Statistics at Harvard University and spending three years teaching at Duke University. She has taught a variety of statistics classes, including a special course for graduate students on "The Art and Practice of Teaching Statistics", and helped co-develop a new 100-level course at Harvard designed to make statistics enjoyable and applicable to real life. She won the Derek C. Bok Award for Excellence in the Teaching of Undergraduates. She has a particular interest in causal inference, statistics education, and applications of statistics in psychology, education, and health.

Eric F. Lock is an assistant professor of Biostatistics at the University of Minnesota School of Public Health. He received his Ph.D. in Statistics from the University of North Carolina in 2012, and spent two years doing a post doc in statistical genetics at Duke University. He has been an instructor and instructional assistant for multiple introductory statistics courses, ranging from very traditional to more progressive. He has a particular interest in machine learning and the analysis of high-dimensional data, and has conducted research on applications of statistics in genetics and medicine.

Dennis F. Lock recently followed his interests in sports statistics to become the Director of Analytics for the Miami Dolphins (football team). He finished his Ph.D. focusing on sports statistics with the Department of Statistics at Iowa State University where he served as a statistical consultant for several years and received the Dan Mowrey Consulting Excellence Award. In 2014 he helped design and implement a randomized study at Iowa State to compare the effectiveness of randomization and traditional approaches to teaching introductory statistics.

Table of Contents

Preface xi

Unit A: Data 1

Chapter 1. Collecting Data 2

1.1. The Structure of Data 4

1.2. Sampling from a Population 17

1.3. Experiments and Observational Studies 31

Chapter 2. Describing Data 52

2.1. Categorical Variables 54

2.2. One Quantitative Variable: Shape and Center 72

2.3. One Quantitative Variable: Measures of Spread 86

2.4. Boxplots and Quantitative/Categorical Relationships 103

2.5. Two Quantitative Variables: Scatterplot and Correlation 117

2.6. Two Quantitative Variables: Linear Regression 136

2.7. Data Visualization and Multiple Variables 152

Unit A: Essential Synthesis 177

Review Exercises 190

Projects Online

Unit B: Understanding Inference 211

Chapter 3. Confidence Intervals 212

3.1. Sampling Distributions 214

3.2. Understanding and Interpreting Confidence Intervals 232

3.3. Constructing Bootstrap Confidence Intervals 248

3.4. Bootstrap Confidence Intervals Using Percentiles 263

Chapter 4. Hypothesis Tests 278

4.1. Introducing Hypothesis Tests 280

4.2. Measuring Evidence with P-values 295

4.3. Determining Statistical Significance 316

4.4. A Closer Look at Testing 333

4.5. Making Connections 349

Unit B: Essential Synthesis 371

Review Exercises 381

Projects Online

Unit C: Inference with Normal and t-Distributions 399

Chapter 5. Approximating with a Distribution 400

5.1. Hypothesis Tests Using Normal Distributions 402

5.2. Confidence Intervals Using Normal Distributions 417

Chapter 6. Inference for Means and Proportions 430

6.1. Inference for a Proportion

6.1-D Distribution of a Proportion 432

6.1-CI Confidence Interval for a Proportion 435

6.1-HT Hypothesis Test for a Proportion 442

6.2. Inference for a Mean

6.2-D Distribution of a Mean 448

6.2-CI Confidence Interval for a Mean 454

6.2-HT Hypothesis Test for a Mean 463

6.3. Inference for a Difference in Proportions

6.3-D Distribution of a Difference in Proportions 469

6.3-CI Confidence Interval for a Difference in Proportions 472

6.3-HT Hypothesis Test for a Difference in Proportions 477

6.4. Inference for a Difference in Means

6.4-D Distribution of a Difference in Means 485

6.4-CI Confidence Interval for a Difference in Means 488

6.4-HT Hypothesis Test for a Difference in Means 494

6.5. Paired Difference in Means 502

Unit C: Essential Synthesis 513

Review Exercises 525

Projects Online

Unit D: Inference for Multiple Parameters 543

Chapter 7. Chi-Square Tests for Categorical Variables 544

7.1. Testing Goodness-of-Fit for a Single Categorical Variable 546

7.2. Testing for an Association between Two Categorical Variables 562

Chapter 8. ANOVA to Compare Means 578

8.1. Analysis of Variance 580

8.2. Pairwise Comparisons and Inference after ANOVA 604

Chapter 9. Inference for Regression 614

9.1. Inference for Slope and Correlation 616

9.2. ANOVA for Regression 632

9.3. Confidence and Prediction Intervals 645

Chapter 10. Multiple Regression 652

10.1. Multiple Predictors 654

10.2. Checking Conditions for a Regression Model 670

10.3. Using Multiple Regression 679

Unit D: Essential Synthesis 693

Review Exercises 707

Projects Online

The Big Picture: Essential Synthesis 715

Exercises for the Big Picture: Essential Synthesis 729

Chapter P. Probability Basics 734

P.1. Probability Rules 736

P.2. Tree Diagrams and Bayes’ Rule 748

P.3. Random Variables and Probability Functions 755

P.4. Binomial Probabilities 762

P.5. Density Curves and the Normal Distribution 770

Appendix A. Chapter Summaries 783

Appendix B. Selected Dataset Descriptions 795

Partial Answers 809

Index

General Index 835

Data Index 838

Editorial Reviews

"this text should be familiar to everyone teaching an introductory statistics course or considering a textbook for one. Truly innovative textbooks are rare indeed, and I hope this one will stimulate much discussion and debate." (Mathematical Association of America 2016)

From the Publisher