Statistics: Unlocking the Power of Data, 1st Edition

ISBN: 9780470601877

This book is being used at:

Statistics: Unlocking the Power of Data, 1st Edition teaches you to collect, analyse and draw conclusions from data. Real data and real applications are used in the text, with exercises chosen specifically to appeal to students. This textbook is integrated with StatKey, a free online user-friendly tool that illustrates key ideas. There are also video tutorials and solutions available to cover key learning goals.


This text comes with WileyPLUS, where you can access an online version of the textbook and a comprehensive collection of self-study tools that will help you ace your exams. Go Tutorial questions allow you to work through complex questions step-by-step, providing assistance if you become stuck. By offering varied questions and instantaneous feedback to your answers, WileyPLUS makes every study session successful.


  • Technology: A set of online interactive dynamic tools, called StatKey, is fully integrated with the text and is available on the web to illustrate key ideas and provide support for computer intensive procedures.
  • Examples and Exercises: Applications in the text are drawn from a wide variety of disciplines. Problems and exercises are plentiful and span a very wide range of difficulty levels, from very straightforward short answer problems to extended projects.
  • Essential Synthesis: Integration of the parts into a coherent whole is also essential. To address this, sections called Essential Synthesis end each unit, in which you are asked to take a step back and look at the big picture. These integration sections will help you prepare for the kind of statistical thinking that you are likely to encounter after finishing your course.
Robin H. Lock is Burry Professor of 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, and a member of the committee that developed GAISE (Guidelines for Assessment and Instruction in Statistics Education). He has won the national Mu Sigma Rho Statistics Education award and numerous awards for presentation on statistics education at national conferences.

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 American, 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.

Kari Lock Morgan received her Ph.D. in statistics from Harvard University in 2011 and is Assistant Professor of the Practice in the Department of Statistical Science at Duke University. While at Harvard, she won the university-wide Derek C. Bok Award for Excellence in the Teaching of Undergraduates. She has a particular interest in causal inference and applications of statistics in education, psychology and health.

Eric F. Lock completed his Ph.D. in statistics at the University of North Carolina in Chapel Hill in 2012, where he received a Departmental Teaching Award. He is now a post-doctoral research fellow at Duke University in a joint appointment with the Departments of Statistical Science and Medical Genetics.

Dennis F. Lock is working on his Ph.D. in statistics in the Department of Statistics at Iowa State University. He has served as a statistical consultant for three years and received the Dan Mowrey Consulting Excellence Award. He has a strong interest in statistics in sports, with a particular passion for the emerging field of statistics in hockey.

Unit A: Data
1. Collecting Data
2. Describing Data

Unit B: Understanding Inference
3. Confidence Intervals
4. Hypothesis Tests

Unit C: Inference for Means and Proportions
5. Approximating with a Distribution
6. Inference for Means and Proportions

Unit D: Inference for Multiple Parameters
7. Chi-Square Tests for Categorical Variables
8. ANOVA for Comparing Means
9 Inference for Regression
10. Multiple Regression

11. Probability Basics

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