Statistics: Unlocking the Power of Data, 3rd Edition

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Statistics: Unlocking the Power of Data, 3rd Edition

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

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$69  USD | $99  CAN

Statistics: Unlocking the Power of Data, 3rd Edition, now with corequisite support content, 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.

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Lecture, Tutorial, and Example Walkthrough videos.

A comprehensive video set created exclusively by the author team includes chapter walkthroughs, videos to explain each learning objective in detail, and solution walkthroughs for every example throughout the text.

Video Questions

Homework questions feature a link to video solutions, or to video tutorials on the learning goal to give students full support as they work through each section.

Corequisite Support Appendices

have been added to the WileyPlus experience. This prerequisite review content includes topics specifically identified as helpful for success in an introductory statistics course and is presented with statistical context where appropriate to prepare students for the material ahead. Instructors will find a wealth of exercises to choose from for each topic, allowing them to pick and choose the right topics to support their corequisite model. Worksheets are also available for each prerequisite topic.

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    • Adaptive Assignments Powered by Knewton ignite students’ confidence to persist so that they can succeed in their courses and beyond. By continuously adapting to each student’s needs and providing achievable goals with just-in-time instruction, Adaptive Assignments close knowledge gaps to accelerate learning.
    • Corequisite Support Appendices have been added to the WileyPlus experience. This prerequisite review content includes topics specifically identified as helpful for success in an introductory statistics course and is presented with statistical context where appropriate to prepare students for the material ahead. Instructors will find a wealth of exercises to choose from for each topic, allowing them to pick and choose the right topics to support their corequisite model. Worksheets are also available for each prerequisite topic.
    • Comprehensive Video Set The author team has continued to revise and add videos for this edition to ensure coverage for EVERY chapter, section, and example in the text.
    • Updated Question Types Drag and Drop questions have been updated to provide more sophisticated options for quizzing and homework.

    Additional Features Include

    • StatKey features a set of statistical applets used to simulate and visualize data. It was developed by the author team and can be used with the data sets that come with the text, or by uploading your own favorite example.

    Instructor Resources

    Instructor Resources give you access to tools and resources available for this text, including:
    • Instructor’s Manual
    • TestGen Computerized Test Bank
    • Instructor’s Solutions Manual
    • PowerPoint Lecture Slides
    • Clicker Questions
    • Class Activity Handouts
    • Computerized Test Bank
    • Printed Test Bank
    • Software Manuals
    • Projects
    • Chapter Summaries
    • 2e to 3e Problem Correlation Guide
    • WileyPLUS Question Index
    • Data Set Descriptive Guide
    • Instructor Video Index
    • Applications Index

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 courses across the spectrum of mathematics and statistics and enjoys collaborating with undergraduates on her research in graph theory. She received her B.A. from Colgate University and her Ph.D. from the University of Massachusetts at Amherst.

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 Award 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.

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 she 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 is particularly interested 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  is currently working for the NFL with the Buffalo Bills, with past experience as Director of Analytics for the Miami Dolphins and in NHL hockey with the Ottawa Senators. 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.

Chapter 1 – Collecting Data
Chapter 2 – Describing Data
Unit A – Essential Synthesis
Chapter 3 – Confidence Intervals
Chapter 4 – Hypothesis Tests
Unit B – Essential Synthesis
Chapter 5 – Approximating with a Distribution
Chapter 6 – Inference for Means and Proportions
Unit C – Essential Synthesis
Chapter 7 – Chi-Square Tests for Categorical Variables
Chapter 8 – ANOVA to Compare Means
Chapter 9 – Inference for Regression
Chapter 10 – Multiple Regression
Unit D: Essential Synthesis
Unit E: The Big Picture
Chapter P – Probability Basics

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