Linear Models and Matrix Algebra with R for Life Sciences

Understanding complex biological and health data is crucial for scientific discovery. This course provides the essential quantitative tools to make sense of your datasets. By the end of this course, you will be able to confidently build, interpret, and critically evaluate linear models for various data analysis tasks in life sciences, leveraging the power of the R programming language. What you'll learn: Understand core principles of linear models and their underlying assumptions. Apply matrix algebra operations fundamental to statistical modeling. Practice implementing linear regression models using R for biological data. Interpret model outputs, assess model fit, and validate assumptions effectively. Learn to prepare and clean real-world life sciences datasets for analysis in R. Develop reproducible analytical workflows for robust scientific inquiry. The course begins with foundational statistical concepts and matrix algebra, progressing to the practical application of linear models in R. You will then learn to interpret results and ensure the validity of your analyses through structured, text-based lessons and coding exercises. This course is designed for absolute beginners with no prior experience in linear models, matrix algebra, or R programming. No prerequisites are required. Start your journey to mastering data analysis in life sciences today.