Biostatistics for Life Science.

Catalog description

This course provides advanced training in the application and interpretation of statistical methods for analyzing biological data. Emphasis is placed on the selection, implementation, and critical evaluation of statistical methodologies commonly used in the life sciences, with extensive use of the R programming language. Topics include contingency table analysis, linear and generalized linear models, ANOVA, maximum likelihood estimation, hypothesis testing (including t-tests and chi-square tests), and survival analysis. Students will engage in the analysis of complex, real-world biological datasets and will be expected to interpret results in a biological and clinical context. Additional emphasis will be placed on experimental design, reproducibility, and the critical evaluation of statistical approaches in primary scientific literature.