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.

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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