Linear Models and Matrix Algebra in R for the Life Sciences

In the life sciences, experimental data is growing increasingly complex, making robust statistical modeling an essential skill for researchers and analysts. This text-based course guides you through the fundamental mathematical and computational tools required to analyze biological data with confidence. You will transition from basic statistical concepts to building and interpreting structured linear models using the R programming language. By completing this course, you will understand how to represent complex experimental designs as mathematical matrices and solve them programmatically to extract meaningful scientific insights. You will gain a clear, conceptual understanding of the mathematics behind regression and analysis of variance, bypassing black-box software tools to write your own transparent analysis workflows. What you'll learn: - Understand the foundational concepts of matrix algebra, including matrix multiplication, identity matrices, and inverses - Represent biological experimental designs, such as multi-group comparisons, as design matrices in R - Fit linear regression models to life sciences data and interpret the resulting coefficients - Apply modern R packages and tidyverse workflows to clean, organize, and prepare high-throughput data for modeling - Formulate and test scientific hypotheses using analysis of variance (ANOVA) and linear contrast methods - Diagnose model fit using diagnostic plots and identify potential outliers or violations of statistical assumptions This course begins with essential terminology, introducing the core concepts of variables, vectors, and matrices before progressing to hands-on modeling scenarios. Through clear explanations and step-by-step code walkthroughs, you will learn how to translate biological questions into statistical frameworks and implement them using modern R practices. This course is designed for beginners in data analysis, life science students, researchers, and lab technicians who want to build a strong mathematical foundation for statistical modeling without needing a prior background in advanced linear algebra. Start learning today to unlock the mathematical foundations of biological data analysis.