Introduction to Statistical Investigations, Second Edition

Book Cover

Introduction to Statistical Investigations, Second Edition

Nathan Tintle, Beth L. Chance, Soma Roy, George W. Cobb, Allan J. Rossman, San Luis Obispo, Todd Swanson, and Jill VanderStoep

SINGLE-TERM
$69  USD | $99  CAN

Introduction to Statistical Investigations provides a unified framework for explaining variation across study designs and variable types, helping students increase their statistical literacy and appreciate the indispensable role of statistics in scientific research. Requiring only basic algebra as a prerequisite, the program uses the immersive, simulation-based inference approach for which the author team is known. Students engage with various aspects of data collection and analysis using real data and clear explanations designed to strengthen multivariable understanding and reinforce concepts.

Each chapter follows a coherent six-step statistical exploration and investigation method (ask a research question, design a study, explore the data, draw inferences, formulate conclusions, and look back and ahead) enabling students to assess a variety of concepts in a single assignment. Challenging questions based on research articles strengthen critical reading skills, fully worked examples demonstrate essential concepts and methods, and engaging visualizations illustrate key themes of explained variation. The end-of-chapter investigations expose students to various applications of statistics in the real world using real data from popular culture and published research studies in variety of disciplines. Accompanying examples throughout the text, user-friendly applets enable students to conduct the simulations and analyses covered in the book.

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Explorations and Investigations Workbook

To facilitate active learning, each chapter contains many Explorations for the students to complete. These materials allow for a variety of instructor-determined approaches to content delivery, including approaches where examples or concepts are presented first by the instructor, then explored by the student, or vice versa. This workbook allows an assignable, collectible PDF format.

Author-Created Chapter Videos

Created at the learning objective level, these videos explain and reinforce key concepts for students.

Author-Created Example Walkthrough Videos

Created for examples throughout the text, these videos explain and reinforce key concepts for students by walking them through each example in detail.

ISI Applets

Rather than asking students to learn to use a statistical software package, the author team has designed easy-to-use web applets that enable students to conduct simulations and perform the analyses presented in this book. Instructors may also ask students to use a commercial software package, but this is not required. A guide to using these applets available for instructor reference.

    What’s New

  • Videos have been updated to support additional and revised learning objectives and content.
  • Additional auto-graded exercises have been added throughout the text.
  • A new, optional (online-only) chapter 11 has been added with formal treatment of probability.
  • Additional Features Include

  • TestGen Computerized Test Bank This new digital test bank allows for quick and easy creation of multiple versions of tests aligned with course content.

Nathan L. Tintle is associate professor of statistics at Dordt College. He has led efforts to develop and institutionalize randomization-based curricula at two institutions (Hope College, 2005–2011 and Dordt, 2011–present), and currently leads the curriculum development project. He has been an invited panelist for several statistics education sessions at national meetings, was recently a member of the executive committee of the Section of Statistical Education of the ASA, received the 2013 Waller Education Award for teaching and innovation in introductory statistics, and was a member of a national advisory committee to the ASA President on training the next generation of statisticians. He has co-authored several articles on student learning using the randomization curriculum, one of which recently won an award for best paper of the year from the Journal of Statistics Education.

Beth L. Chance is Professor of Statistics at California Polytechnic State University. She is also co-author with Allan Rossman of the Workshop Statistics series. She has published articles on statistics education in The American Statistician, Journal of Statistics Education, and the Statistics Education Research Journal. She has also collaborated on several chapters and books aimed at enhancing teacher preparation to teach statistics and has been involved for many years with the Advanced Placement Statistics program. She is a Fellow of the American Statistical Association and received the 2002 Waller Education Award for Excellence and Innovation in Teaching Undergraduate Statistics. The Rossman/Chance collection of online applets for exploring statistical concepts was awarded the 2009 CAUSEweb Resource of the Year Award and a 2011 MERLOT Award for Exemplary Learning Materials.

George W. Cobb is Robert L. Rooke Professor Emeritus of Statistics at Mount Holyoke College and has extensive knowledge of statistics education, expertise in developing imaginative and innovative curricular materials, and the honor of having brought the conversation on randomization-based approaches in introductory statistics to the mainstream via his 2005 USCOTS presentation and 2007 paper. He served as the first chair of the Joint Committee on Undergraduate Statistics of the American Mathematical Association and American Statistical Association (1991–98), editing that committee’s 1992 report, “Teaching Statistics.” He served for three years on the National Research Council’s Committee on Applied and Theoretical Statistics and recently served as vice-president of the American Statistical Association. He is a Fellow of the ASA and received the ASA’s Founders Award in 2007. He has published/edited a number of books.

Allan J. Rossman is professor and chair of the statistics department at California Polytechnic State University. He earned a Ph.D. in Statistics from Carnegie Mellon University. He is co-author with Beth Chance of the Workshop Statistics series and Investigating Statistical Concepts, Applications, and Methods, both of which adopt an active learning approach to learning introductory statistics. He served as program chair for the 2007 Joint Statistical Meetings, as president of the International Association for Statistical Education from 2007 to 2009, and as chief reader for the Advanced Placement program in statistics from 2009 to 2014. He is a fellow of the American Statistical Association and received the Mathematical Association of America’s Haimo Award for Distinguished College or University Teaching of Mathematics in 2010.

Soma Roy is associate professor of statistics at California Polytechnic State University. She is editor of the Journal of Statistics Education and has presented talks related to the randomization-based curriculum and student learning at national meetings. She writes and reviews assessment tasks for the Illustrative Mathematics Project, an initiative to support adoption of the K-12 core standards for statistics. She co-leads, with her colleagues at Cal Poly, a teacher-preparation workshop for AP Statistics teachers. She also has an active research program in health statistics involving undergraduates.

Todd M. Swanson is associate professor of mathematics at Hope College. He is a co-author of Precalculus: A Study of Functions and their Applications, Understanding Our Quantitative World and Projects for Precalculus, which was an INPUT Award winner. He has published articles in the Journal of Statistics Education, Statistics Education Research Journal, and Stats: The Magazine for Students of Statistics. He has presented at numerous national meetings, workshops, and mini-courses about innovative ways to teach mathematics and statistics that focus on guided-discovery methods and projects.

Jill L. Vanderstoep is Adjunct Assistant Professor of Mathematics at Hope College. She has participated in efforts to develop and implement randomization-based curricula at Hope College since 2005. She has presented on the curriculum and assessment results at national conferences and has co-led workshops on introducing and implementing the randomization-based curriculum. She has co-authored two articles looking at student learning differences between randomization-based curriculum and traditional curriculum. She has extensive experience in the evaluation of assessment data to drive curricular reform.

Preliminaries – Introduction to Statistical Investigations
Chapter 1 – Significance: How Strong is the Evidence
Chapter 2 – Generalization: How Broadly Do the Results Apply?
Chapter 3 – Estimation: How Large is the Effect?
Chapter 4 – Causation: Can We Say What Caused the Effect?
Chapter 5 – Comparing Two Proportions
Chapter 6 – Comparing Two Means
Chapter 7 – Paired Data: One Quantitative Variable
Chapter 8 – Comparing More Than Two Proportions
Chapter 9 – Comparing More Than Two Means
Chapter 10 – Two Quantitative Variables
Chapter 11 – Modeling Randomness (ONLINE ONLY)