Introduction to Statistics

Purpose: to help other instructors teaching the same course

Common Course ID:  MATH 110
CSU Instructor Open Textbook Adoption Portrait

Abstract: The OER materials offer an AI-Integrated approach for teaching introductory statistics courses, including lectures, activities, and homework that can be easily modified by other instructors. The main motivation for the course creation, by Kathy Gray in the Department of Math and Statistics at Chico State, was a desire to promote AI literacy and to help students use AI as a tool to support their learning while creating a more personalized course experience. Instructors can access materials in Merlot and implement this course within a learning management system such as Canvas. This OER creation is especially suited for an online course. 

Introduction to Statistics.  MATH 110
Brief Description of course highlights:  Math 105 focuses on helping students understand and apply basic statistical concepts. The course covers topics like descriptive statistics, normal distribution, sampling methods, confidence intervals, hypothesis testing and linear regression. StatCrunch is used throughout the course to analyze real datasets, create graphs, and perform statistical tests. This allows students to focus more on interpreting results and understanding concepts rather than manual calculations. The course emphasizes real-world applications, data-driven decision-making, and developing statistical reasoning skills.

Student population:  Math 105 serves a variety of majors. Students usually take this course as freshmen to fulfill their GE requirement.

Learning or student outcomes:  Upon completion of the course students will:
SLO 1: Identify cases, variables, and types of variables in data sets, including multivariable data sets.
SLO 2: Explain how study design affects what conclusions can be drawn from the data. In particular, explain the importance of random sampling and random assignment.
SLO 3: Identify and interpret appropriate graphs and summary statistics in both univariate and multivariate settings.
SLO 4: Use statistical software or apps to create visualizations, produce summary statistics, and carry out the computational aspects of statistical analysis.
SLO 5: Articulate the role that probability and random variation plays in statistical inference. In particular, articulate its role in generalizing from a sample to a population.
SLO 6: Identify the key methods to use in statistical inference and interpret results from those methods.
SLO 7: Recognize that sampling variation is one reason that conclusions drawn from statistical inference might be incorrect, and that replication and reproducibility are important to verify conclusions.
SLO 8: Use statistical models, such as linear regression, for prediction.
SLO 9: Recognize ethical issues and dilemmas in statistical practice.
SLO 10: Communicate, clearly and in context, results obtained from data.
SLO 11: Demonstrate a responsible and effective use of AI tools by applying them to organize learning materials, conduct data analysis, and communicate problem-solving processes clearly.

Textbook or OER/Low cost Title: AI-Integrated Introduction to Statistics

Brief Description:  This AI-integrated introductory statistics textbook is designed to help students build a strong foundation in statistical thinking through a blend of core concepts and interactive learning. It covers key topics such as descriptive statistics, normal distributions, linear regression, confidence intervals, and hypothesis testing, while incorporating tools like StatCrunch for hands-on data analysis and visualization. The pedagogical approach emphasizes conceptual understanding over rote calculation, using real-world datasets, guided examples, and AI-driven support to provide immediate feedback and personalized explanations.

The textbook includes guided chapter notes, weekly activities, weekly homework, and weekly knowledge checks. Custom GPTs have been created for each lecture and activity providing each student with a personalized course experience. Custom GPTs can be easily edited by instructors. 

Please provide a link to the resource  
https://www.merlot.org/merlot/viewSite.htm?id=9168439 

Authors:  Kathy Gray, PhD

Student access:  All materials can be accessed at the Merlot website
https://www.merlot.org/merlot/viewSite.htm?id=9168439

Supplemental resources:  The resources included are: course notes for each chapter; StatCrunch instructions; custom GPTs for all course notes, homeworks, activities, and knowledge checks.

Provide the cost savings from that of a traditional textbook.  Students taking this course will need to purchase StatCrunch for $19.99 and a course packet for $25 thus bringing the total for the course to about $45. A traditionally priced Intro Stat course would cost the student over $100.

License: Creative Commons Zero 1.0 (CC0 1.0) Universal Public Domain Dedication 

OER/Low Cost Adoption Process
Please provide an explanation or what motivated you to use this textbook or OER/Low Cost.  Introductory statistics courses are a persistent barrier to student success, often marked by high DWF rates and large equity gaps. This project tackles these challenges through a redesign of the course that emphasizes both innovative instruction and the use of low-cost, accessible materials. By integrating ChatGPT Edu, the redesign replaces traditional, often disengaging online lectures with custom GPTs that provide an interactive, adaptive learning experience without increasing student costs. This learner-centered approach is intended to deepen build AI literacy and strengthen critical thinking skills. By focusing on a well-known bottleneck course while prioritizing affordability and access, the project advances CSU’s equity and graduation goals and better prepares students for a data-driven world.

How did you find and select the open textbook for this course?  I previously created the chapter notes. I created much of the material myself; however, I also used OER materials such as OpenIntro Statistics. 

Sharing Best Practices:  I would suggest seeking training on OER materials so that you know what openly licensed sources exist.
Describe any challenges you experienced, and lessons learned.  One of the main challenges in developing the AI-integrated materials was their nontraditional format which made it difficult to determine how best to share the materials with other instructors. Additionally, due to CSU Chico administrative restrictions, I am unable to distribute my custom GPTs outside of Chico State. As a result, I needed to identify alternative ways to make the materials accessible to individuals beyond my institution.

Instructor Name - Kathy Gray
I am a professor in the Department of Math and Statistics at Chico State.

Please provide a link to your university page.
https://apps.csuchico.edu/directory/Employee/klgray

Please describe the courses/course numbers that you teach.
Math 105-Introduction to Statistics
Math 314-Probability and Statistics for Science and Technology
Math 315-Applied Statistical Methods
Math 350-Introduction to Probability and Statistics
Math 351-Introduction to Probability and Statistics II
Math 450-Computational Statistics
Math 456-Applied Statistical Methods II
Math 458-Sampling
Math 651-Probability and Statistics for Data Science

Describe your teaching philosophy and any research interests related to your discipline or teaching.  My teaching philosophy centers on helping students develop conceptual understanding through active learning, real-world applications, and meaningful engagement with data. I am particularly interested in ways to foster AI literacy and teaching students to use AI responsibly and critically as a tool for learning.