Biostatistical Methods I: Linear Regression and ANOVA
BIOL-40315
This fully online, asynchronous course provides a comprehensive introduction to linear regression and analysis of variance (ANOVA), equipping learners with essential skills for statistical modeling and data analysis in biomedical and scientific fields.
Course Highlights:
- Core Statistical Techniques: Master simple and multiple linear regression to predict outcomes and ANOVA to compare group differences, with practical applications in clinical research, public health, and beyond.
- Data Transformation Skills: Learn to apply logarithmic transformations to address non-normality and stabilize variance, using diagnostic tools like residual plots and Q-Q plots.
- R Programming Proficiency: Gain hands-on experience with R for data analysis, building and evaluating models without requiring prior coding knowledge.
- Flexible Learning: Access course materials at your convenience during the session, ideal for balancing professional or academic commitments.
Participants will develop the ability to construct, interpret, and refine statistical models, preparing them for advanced study or careers in biostatistics, data science, or research. This course establishes a strong foundation in biostatistics basics, setting the stage for further exploration in subsequent courses.
Who Should Enroll:
- Students seeking a linear regression course or ANOVA training to support academic goals.
- Professionals aiming to enhance statistical analysis skills for research or industry roles.
- Individuals interested in biostatistics or data analytics with applications in healthcare and science.
- Course Materials: A course reader is provided with each module, accessible to students at no additional cost. Instructions for accessing these materials are shared on the first day.
- Prerequisites: No prior experience with R or advanced statistics is required.
- Next Steps: Continue your learning with Biostatistical Methods II: Logistic Regression and Survival Analysis to explore advanced topics such as logistic regression and survival analysis.
Course Information
Course sessions
Section ID:
Class type:
This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
Textbooks:
No textbook required.
Policies:
- No refunds after: 7/7/2025
Schedule:
Instructor: Arthur Li, Master of Science
Biostatistician, City of Hope; Instructor, Department of Preventative Medicine, USC
Arthur Li holds an M.S. in Biostatistics from the University of Southern California and serves as a biostatistician at City of Hope National Medical Center, where he supports cancer research by analyzing clinical and genomic data. At USC, he developed and taught SAS and R programming courses and occasionally taught a linear regression course, helping students build data analysis skills. At UC San Diego Division of Extended Studies, Li developed and teaches the Biostatistical Methods series courses, transitioned from SAS to R, assisting learners in exploring biostatistics, alongside other R programming courses. He authored the Handbook of SAS® DATA Step Programming (CRC Press, 2013), a resource for data management in SAS. In his spare time, Li enjoys traveling, cooking, and exploring new cultures.
Section ID:
Class type:
This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
Textbooks:
All course materials are included unless otherwise stated.
Policies:
- No refunds after: 10/7/2025
Schedule:
Instructor: Arthur Li, Master of Science
Biostatistician, City of Hope; Instructor, Department of Preventative Medicine, USC
Arthur Li holds an M.S. in Biostatistics from the University of Southern California and serves as a biostatistician at City of Hope National Medical Center, where he supports cancer research by analyzing clinical and genomic data. At USC, he developed and taught SAS and R programming courses and occasionally taught a linear regression course, helping students build data analysis skills. At UC San Diego Division of Extended Studies, Li developed and teaches the Biostatistical Methods series courses, transitioned from SAS to R, assisting learners in exploring biostatistics, alongside other R programming courses. He authored the Handbook of SAS® DATA Step Programming (CRC Press, 2013), a resource for data management in SAS. In his spare time, Li enjoys traveling, cooking, and exploring new cultures.
