Biostatistical Methods I: Linear Regression and ANOVA

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.

Learning Outcomes: 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.

Additional Information: 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.