MATH 2265 - Statistics with Applications

Purpose: to help other instructors teaching the same course

Common Course ID:  MATH 2265
CSU Instructor Open Textbook Adoption Portrait

Abstract: This open textbook is being utilized in a mathematics course for undergraduate or graduate students by Dr. Youngsu Kim at California State University, San Bernardino. The open textbook provides a comprehensive introduction to statistical reasoning with real data applications, supplemented by instructor-developed lecture slides, in-class worksheets, interactive PlayPosit videos, and hands-on R/RStudio exercises. The main motivation to adopt an open textbook was to provide high-quality, affordable materials that foster data literacy and practical statistical skills. Most students access the open textbook in Canvas.

MATH 2265 - Statistics with Applications
Brief Description of course highlights:  MATH 2265 is an introductory statistics course covering data acquisition, organization, evaluation, and visual representation. Topics include multivariate data, correlation and regression, estimation of parameters, classical hypothesis testing, and computer-based simulations including resampling methods. The emphasis is on applications to real data. See the CSUSB course catalog at https://catalog.csusb.edu/coursesaz/math/.

Student population: Students are primarily computer science and engineering majors, though the course serves a diverse range of undergraduate students. They typically have completed MATH 2210 (Calculus) as a prerequisite. The class is mixed with students at varying levels of prior statistical exposure.

Learning or student outcomes: The CSUSB Mathematics Department is committed to developing and measuring the outcomes of our teaching efforts. Please see the following website for a list of these outcomes: https://www.csusb.edu/mathematics/undergraduate/advising
Goal 1: Students will demonstrate a conceptual understanding of mathematics.
1.1. Students will demonstrate an understanding of fundamental concepts, algorithms, operations, and relations.
Goal 2: Students will attain procedural fluency in mathematics.
2.1. Students will correctly apply mathematical theorems, properties and definitions.
Goal 3: Students will demonstrate adaptive reasoning and problem-solving skills in mathematics.
3.1. Students will justify solutions using a variety of strategies and representations.

Key challenges faced and how resolved:  Challenge: Commercial statistics textbooks are expensive, yet instructors typically cover only 30–40% of the content. New editions are released frequently with minor modifications.

Resolution: Adopted the OER textbook OpenIntro Statistics supplemented with MyOpenMath for auto-graded homework, instructor-created worksheets for in-class group work, PlayPosit interactive videos for asynchronous review, and R/RStudio exercises with instructor-created videos to build data analysis skills.

Textbook or OER Title: OpenIntro Statistics (3rd Ed) 

Brief Description:  OpenIntro Statistics provides a thorough introduction to statistical reasoning with an emphasis on real-world data applications. It includes clear explanations, numerous examples, and exercises that range from conceptual to computational. The text covers data visualization, probability, distributions, confidence intervals, hypothesis testing, and regression. The instructor supplemented the text with custom lecture slides, in-class worksheets, and interactive video modules.

Please provide a link to the resource  https://www.openintro.org/book/os   

Authors: David Diez, Christopher Barr, and Mine Çetinkaya-Rundel

Student access:  All course materials are accessed through Canvas. External links to the OpenIntro Statistics PDF, MyOpenMath, and RStudio Cloud are embedded there. No purchase or separate account registration is required for the textbook; MyOpenMath is free for students.

Supplemental resources:  
MyOpenMath (free online homework system)
Instructor-created worksheets (LaTeX, managed on Overleaf)
PlayPosit interactive videos
R/RStudio exercises with instructor-created video tutorials
Lecture slides (Remark JS / Markdown)
Gradescope (for assignment submission and feedback)

Provide the cost savings from that of a traditional textbook. Single section (Fall 2025): $1,500 = $50 × 30 students.  Overall savings across multiple sections and terms: $8,300 = $50 × 166 students (Fall 2024: 34 students; Spring 2025: 29 and 28 students; Fall 2025: 35 students).

License*:  Creative Commons Attribution-ShareAlike 3.0 Unported License (CC BY-SA 3.0)

OER/Low Cost Adoption Process
Please provide an explanation or what motivated you to use this textbook or OER/Low Cost. To reduce financial barriers while maintaining instructional quality. The OER platform allowed alignment with course learning outcomes and enabled deeper student engagement through hands-on data analysis and interactive tools. Data literacy is a crucial skill, and confidence interval interpretation is ubiquitous across disciplines.

How did you find and select the open textbook for this course? The instructor evaluated several OER statistics textbooks and selected OpenIntro Statistics for its comprehensive coverage, real-data emphasis, and compatibility with a free online homework system (MyOpenMath). The textbook is widely adopted and peer-reviewed.

Sharing Best Practices:  Nothing is perfect. Listen to the students. Start with the core OER text and build supplemental materials (worksheets, videos, coding exercises) gradually based on student feedback. Integration of OER and LMS systems (like Canvas, MyOpenMath, PlayPosit) takes preparation time but pays off in student engagement.

Describe any challenges you experienced, and lessons learned. Exploring, modifying, and adopting materials from various sources requires significant preparation time. MyOpenMath has a great problem library, but writing custom problems is time-consuming. Students respond well to group work in the classroom and to instructor-created video tutorials paired with R exercises.

Instructor Name - Youngsu Kim 
I am an Associate Professor of Mathematics at California State University, San Bernardino. I teach undergraduate and graduate mathematics courses, including statistics, discrete mathematics, differential equations, and abstract algebra.

Please provide a link to your university page.
https://www.csusb.edu/mathematics/faculty-staff

Please describe the courses/course numbers that you teach.
MATH 2265 — Statistics with Applications
MATH 2732 — Discrete Mathematics
MATH 2270/4270 — Differential Equations I & II
MATH 1201 — Introduction to Statistical Thinking
MATH 4600/6016 — Abstract Algebra and Graduate Algebra

Describe your teaching philosophy and any research interests related to your discipline or teaching.  I strive to connect abstract mathematical concepts with concrete examples and real applications. My research interests lie in commutative algebra and algebraic geometry, and I explore ways to integrate open resources and interactive technologies into teaching.