The Regression Cookbook: A Structured Data Science-based Approach to a Comprehensive Regression Analysis Toolbox

Project Title	The Regression Cookbook: A Structured Data Science-based Approach to Learn and Teach a Comprehensive Regression Analysis Toolbox
Principal Investigator	Gilberto Alexi Rodríguez Arelis
Co-Applicants	-Andy Man Yeung Tai, Co-Author, Postdoctoral Teaching and Learning Fellow, Master of Data Science, Department of Statistics, Faculty of Science -Ben Chen, Co-Author, Course Coordinator, Master of Data Science, Department of Computer Science, Faculty of Science
Faculty	Science
Funding Year	2025
Project Summary	Finding relationships within populations or systems through data modelling and using these models to predict key variables is a cornerstone of the public and private sectors. In this context, regression analysis and supervised learning are two pivotal fields in data science, forming a critical synergy where statistics and machine learning intersect. The Regression Cookbook is an open educational resource (OER) that bridges statistics and machine learning. It emphasizes mathematical and computational tools to model data, uncover meaningful relationships, and make key predictions about populations or systems. Uniquely, it presents regression techniques through dual programming perspectives, utilizing both R and Python. This resource is tailored to enhance the learning experience of graduate and undergraduate students interested in data science. Its “cookbook” format prioritizes practical, example-driven learning over purely theoretical approaches (while still providing theoretical sparks for students looking for this material), making it highly engaging. In alignment with UBC’s commitment to OER, the Regression Cookbook seeks to reduce student costs while providing high- quality, freely accessible learning materials. The grant will support this resource’s development, integration, and ongoing maintenance, ensuring its sustainability as a valuable tool for students and educators in data science and statistics courses.
Grant type	OER Affordability Gtant
Funded Amount	$24,948