Statistics: Learning from Data, AP® Edition,
2nd Edition

• Roxy Peck

  Roxy Peck is Associate Dean Emerita of the College of Science and Mathematics, and Professor of Statistics Emerita at California Polytechnic State University, San Luis Obispo. A faculty member at Cal Poly from 1979 until 2009, Roxy served for six years as Chair of the Statistics Department before becoming Associate Dean, a position she held for 13 years. She received an M.S. in Mathematics and a Ph.D. in Applied Statistics from the University of California, Riverside. Roxy is nationally known in the area of statistics education, and she was presented with the Lifetime Achievement Award in Statistics Education at the U.S. Conference on Teaching Statistics in 2009. In 2003, she received the American Statistical Association’s Founder’s Award, recognizing her contributions to K–12 and undergraduate statistics education. She is a Fellow of the American Statistical Association and an elected member of the International Statistics Institute. Roxy served for five years as the Chief Reader for the Advanced Placement (AP) Statistics Exam and has chaired the American Statistical Association’s Joint Committee with the National Council of Teachers of Mathematics on Curriculum in Statistics and Probability for Grades K–12 and the Section on Statistics Education. In addition to her texts in introductory statistics, Roxy is also co-editor of “Statistical Case Studies: A Collaboration Between Academe and Industry” and a member of the editorial board for “Statistics: A Guide to the Unknown, 4th Edition.” Outside the classroom, Roxy likes to travel and spends her spare time reading mystery novels. She also collects Navajo rugs and heads to Arizona and New Mexico whenever she can find the time.

• Tom Short

  The late Tom Short was an Associate Professor in the Statistics Program within the Department of Mathematics at West Chester University of Pennsylvania. He also previously held faculty positions at Villanova University, Indiana University of Pennsylvania and John Carroll University. He was a Fellow of the American Statistical Association and received the 2005 Mu Sigma Rho Statistics Education Award. Tom served on the leadership team for readings of the Advanced Placement (AP) Statistics Exam, and on the AP Statistics Development Committee. He also served on the Board of Directors of the American Statistical Association. Tom treasured the time he shared with his four children and the many adventures experienced with his wife, Darlene.

• Chris Olsen

  Chris Olsen taught statistics at George Washington High School in Cedar Rapids, Iowa, for over 25 years and currently teaches at Grinnell College. Chris is a past member (twice) of the AP Statistics Test Development Committee and has been a table leader at the AP Statistics reading for 14 years. He is a long-time consultant to the College Board and has led workshops and institutes for AP Statistics teachers in the United States and internationally. Chris was the Iowa recipient of the Presidential Award for Excellence in Science and Mathematics Teaching in 1986, a regional awardee of the IBM Computer Teacher of the Year in 1988, and received the Siemens Award for Advanced Placement in Mathematics in 1999. Chris is a frequent contributor to and is moderator of the AP Teacher Community online. He is currently a member of the editorial board of “Teaching Statistics.” Chris graduated from Iowa State University with a major in mathematics and philosophy, and while acquiring graduate degrees at the University of Iowa, he concentrated on statistics, computer programming, and psychometrics. In his spare time he enjoys reading and hiking. He and his wife have a daughter, Anna, a Caltech graduate in Civil Engineering.

• Restructured Chapters on Statistical Inference The chapters on statistical inference have been restructured to include methods for learning from experiments in the same chapter as methods for learning from samples.

• Updated Examples and Exercises The second edition contains many updated examples and exercises on topics of interest to students that use data from recent journal articles, newspapers, and web posts.

• WebAssgin This online learning platform fuels practice, so students truly absorb what they learn—and are better prepared come test time. Videos and tutorials walk them through concepts and deliver instant feedback and grading, so they always know where they stand in class.

• A New Approach to Probability: Research has demonstrated how students develop an understanding of probability and chance. Using natural frequencies to reason about probability, especially conditional probability, is much easier for students to understand. The treatment of probability in this text is complete, including conditional probability and Bayes' Rule type probability calculations.

• Chapter on Overview of Statistical Inference (Chapter 7): This short chapter focuses on the things students need to think about in order to select an appropriate method of analysis. In most texts, these considerations are "hidden" in the discussion that occurs when a new method is introduced.

• An Organization That Reflects the Data Analysis Process: The ordering of topics in the textbook mirrors this process: data collection, then data description, then statistical inference.

• Inference for Proportions before Inference for Means: The book makes it possible to develop the concept of a sampling distribution via simulation. Simulation is simpler in the context of proportions, where it is easy to construct a hypothetical population (versus the more complicated context of means, which requires assumptions about shape and spread).

• Chapter Learning Objectives--Keeping Students Informed about Expectations: The learning objectives explicitly state the expected student outcomes, and are presented in three categories: Conceptual Understanding, Mastery of Mechanics, and Putting It into Practice.

• Preview--Motivation for Learning: Each chapter opens with a Preview and Preview Example that provide motivation for studying the concepts and methods introduced in the chapter. They address why the material is worth learning, provide the conceptual foundation for the methods covered in the chapter, and connect to what the student already knows.

• Real Data That Motivates and Engages: Examples and exercises with overly simple settings don't allow students to practice interpreting results in real situations. The exercises and examples are a particular strength of this text. Most involve data extracted from journal articles, newspapers, and other published sources. They cover a wide range of disciplines and subject areas of interest to today's students, including, among others, health and fitness, consumer research, psychology and aging, environmental research, law and criminal justice, and entertainment.

• Are You Ready to Move On?--Students Test Their Understanding: Prior to moving to the next chapter, "Are You Ready to Move On?" questions allow students to confirm that they have achieved the chapter learning objectives. Like the problem sets for each section, this collection of exercises is developmental--assessing all of the chapter learning objectives and serving as a comprehensive end-of-chapter review.

• Simple Design: Recent research shows that many of the "features" in current textbooks are not really helpful to students. In fact, cartoons, sidebars, historical notes, fake post-it notes in the margins, etc. actually distract students and interfere with learning. STATISTICS: LEARNING FROM DATA has a simple, clean design that minimizes clutter and maximizes student understanding.

• Chapter Activities--Engaging Students in Hands-On Activities: There is a growing body of evidence indicating that students learn best when they are actively engaged. Chapter activities guide students' thinking about important ideas and concepts.

Cengage provides a range of supplements that are updated in coordination with the main title selection. For more information about these supplements, contact your Learning Consultant.

CSM K12AE ONLN STATISTICS LEAR NING FROM DATA AP
9781337913294

K12AE IAC COGNERO STATISTICS L EARNING FROM DATA AP LVL 4
9781337559317

K12AE SSO COGNERO STATISTICS L EARNING FROM DATA AP LVL 4
9781337559331