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Welcome to Introductory Statistics, an OpenStax resource. This textbook was written to increase student access to high-quality learning materials, maintaining highest standards of academic rigor at little to no cost.

The foundation of this textbook is Collaborative Statistics, by Barbara Illowsky and Susan Dean. Additional topics, examples, and innovations in terminology and practical applications have been added, all with a goal of increasing relevance and accessibility for students.

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About Introductory Statistics

Introductory Statistics follows scope and sequence requirements of a one-semester introduction to statistics course and is geared toward students majoring in fields other than math or engineering. The text assumes some knowledge of intermediate algebra and focuses on statistics application over theory. Introductory Statistics includes innovative practical applications that make the text relevant and accessible, as well as collaborative exercises, technology integration problems, and statistics labs.

Coverage and scope

Chapter 1 Sampling and Data
Chapter 2 Descriptive Statistics
Chapter 3 Probability Topics
Chapter 4 Discrete Random Variables
Chapter 5 Continuous Random Variables
Chapter 6 The Normal Distribution
Chapter 7 The Central Limit Theorem
Chapter 8 Confidence Intervals
Chapter 9 Hypothesis Testing with One Sample
Chapter 10 Hypothesis Testing with Two Samples
Chapter 11 The Chi-Square Distribution
Chapter 12 Linear Regression and Correlation
Chapter 13 F Distribution and One-Way ANOVA

Alternate sequencing

Introductory Statistics was conceived and written to fit a particular topical sequence, but it can be used flexibly to accommodate other course structures. One such potential structure, which fits reasonably well with the textbook content, is provided below. Please consider, however, that the chapters were not written to be completely independent, and that the proposed alternate sequence should be carefully considered for student preparation and textual consistency.

Chapter 1 Sampling and Data
Chapter 2 Descriptive Statistics
Chapter 12 Linear Regression and Correlation
Chapter 3 Probability Topics
Chapter 4 Discrete Random Variables
Chapter 5 Continuous Random Variables
Chapter 6 The Normal Distribution
Chapter 7 The Central Limit Theorem
Chapter 8 Confidence Intervals
Chapter 9 Hypothesis Testing with One Sample
Chapter 10 Hypothesis Testing with Two Samples
Chapter 11 The Chi-Square Distribution
Chapter 13 F Distribution and One-Way ANOVA

Pedagogical foundation and features

  • Examples are placed strategically throughout the text to show students the step-by-step process of interpreting and solving statistical problems. To keep the text relevant for students, the examples are drawn from a broad spectrum of practical topics, including examples about college life and learning, health and medicine, retail and business, and sports and entertainment.
  • Try It practice problems immediately follow many examples and give students the opportunity to practice as they read the text. They are usually based on practical and familiar topics, like the Examples themselves.
  • Collaborative Exercises provide an in-class scenario for students to work together to explore presented concepts.
  • Using the TI-83, 83+, 84, 84+ Calculator shows students step-by-step instructions to input problems into their calculator.
  • The Technology Icon indicates where the use of a TI calculator or computer software is recommended.
  • Practice, Homework, and Bringing It Together problems give the students problems at various degrees of difficulty while also including real-world scenarios to engage students.

Statistics labs

These innovative activities were developed by Barbara Illowsky and Susan Dean in order to offer students the experience of designing, implementing, and interpreting statistical analyses. They are drawn from actual experiments and data-gathering processes and offer a unique hands-on and collaborative experience. The labs provide a foundation for further learning and classroom interaction that will produce a meaningful application of statistics.

Statistics Labs appear at the end of each chapter and begin with student learning outcomes, general estimates for time on task, and any global implementation notes. Students are then provided with step-by-step guidance, including sample data tables and calculation prompts. The detailed assistance will help the students successfully apply the concepts in the text and lay the groundwork for future collaborative or individual work.

Additional resources

Student and instructor resources

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Partner resources

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About the authors

Senior contributing authors

Barbara Illowsky, De Anza College
Susan Dean, De Anza College

Contributing Authors

Daniel Birmajer, Nazareth College
Bryan Blount, Kentucky Wesleyan College
Sheri Boyd, Rollins College
Matthew Einsohn, Prescott College
James Helmreich, Marist College
Lynette Kenyon, Collin County Community College
Sheldon Lee, Viterbo University
Jeff Taub, Maine Maritime Academy

Reviewers

Birgit Aquilonius, West Valley College
Charles Ashbacher, Upper Iowa University, Cedar Rapids
Abraham Biggs, Broward Community College
Roberta Bloom, De Anza College
Ernest Bonat, Portland Community College
Sarah Boslaugh, Kennesaw State University
David Bosworth, Hutchinson Community College
George Bratton, University of Central Arkansas
Jing Chang, College of Saint Mary
Laurel Chiappetta, University of Pittsburgh
Lenore Desilets, De Anza College
Ann Flanigan, Kapiolani Community College
David French, Tidewater Community College
Mo Geraghty, De Anza College
Larry Green, Lake Tahoe Community College
Michael Greenwich, College of Southern Nevada
Inna Grushko, De Anza College
Valier Hauber, De Anza College
Janice Hector, De Anza College
Robert Henderson, Stephen F. Austin State University
Mel Jacobsen, Snow College
Mary Jo Kane, De Anza College
Charles Klein, De Anza College
Alexander Kolovos
Sara Lenhart, Christopher Newport University
Wendy Lightheart, Lane Community College
Vladimir Logvenenko, De Anza College
Jim Lucas, De Anza College
Lisa Markus, De Anza College
Miriam Masullo, SUNY Purchase
Diane Mathios, De Anza College
Robert McDevitt, Germanna Community College
Mark Mills, Central College
Cindy Moss, Skyline College
Nydia Nelson, St. Petersburg College
Benjamin Ngwudike, Jackson State University
Jonathan Oaks, Macomb Community College
Carol Olmstead, De Anza College
Adam Pennell, Greensboro College
Kathy Plum, De Anza College
Lisa Rosenberg, Elon University
Sudipta Roy, Kankakee Community College
Javier Rueda, De Anza College
Yvonne Sandoval, Pima Community College
Rupinder Sekhon, De Anza College
Travis Short, St. Petersburg College
Frank Snow, De Anza College
Abdulhamid Sukar, Cameron University
Mary Teegarden, San Diego Mesa College
John Thomas, College of Lake County
Philip J. Verrecchia, York College of Pennsylvania
Dennis Walsh, Middle Tennessee State University
Cheryl Wartman, University of Prince Edward Island
Carol Weideman, St. Petersburg College
Andrew Wiesner, Pennsylvania State University

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© Jun 23, 2022 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.