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Learning Objectives

Introductory Statistics is intended for the one-semester introduction to statistics course for students who are not mathematics or engineering majors. It focuses on the interpretation of statistical results, especially in real world settings, and assumes that students have an understanding of intermediate algebra. In addition to end of section practice and homework sets, examples of each topic are explained step-by-step throughout the text and followed by a Try It problem that is designed as extra practice for students. This book also includes collaborative exercises and statistics labs designed to give students the opportunity to work together and explore key concepts. To support today’s student in understanding technology, this book features TI 83, 83+, 84, or 84+ calculator instructions at strategic points throughout. While the book has been built so that each chapter builds on the previous, it can be rearranged to accommodate any instructor’s particular needs.

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.

About OpenStax

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Errata

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Format

You can access this textbook for free in web view or PDF through Pressbooks.

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 Linear Regression and Correlation
Chapter 4 Probability Topics
Chapter 5 Discrete Random Variables
Chapter 6 Continuous Random Variables
Chapter 7 The Normal Distribution
Chapter 8 The Central Limit Theorem
Chapter 9 Confidence Intervals
Chapter 10 Hypothesis Testing with One Sample
Chapter 11 Hypothesis Testing with Two Samples

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.

 

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About the authors

Senior contributing authors

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

Contributing authors

Birgit Aquilonius, West Valley College
Charles Ashbacher, Upper Iowa University, Cedar Rapids
Abraham Biggs, Broward Community College
Daniel Birmajer, Nazareth College
Roberta Bloom, De Anza College
Bryan Blount, Kentucky Wesleyan College
Ernest Bonat, Portland Community College
Sarah Boslaugh, Kennesaw State University
David Bosworth, Hutchinson Community College
Sheri Boyd, Rollins College
George Bratton, University of Central Arkansas
Jing Chang, College of Saint Mary
Laurel Chiappetta, University of Pittsburgh
Lenore Desilets, De Anza College
Matthew Einsohn, Prescott 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
Jim Helmreich, Marist College
Robert Henderson, Stephen F. Austin State University
Mel Jacobsen, Snow College
Mary Jo Kane, De Anza College
Lynette Kenyon, Collin County Community College
Charles Klein, De Anza College
Alexander Kolovos
Sheldon Lee, Viterbo University
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
Jeffery Taub, Maine Maritime Academy
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

Kelli McCarthy, Arapahoe Community College

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Introduction to Statistics by St. Clair College is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted.

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