 Introduction: the need for statistical analyses and models in biology: Testing relationships among biological/biophysical variables: regression and correlation. Univariate models.
 Testing covariates and multiple variables: multiple linear regression.
 Experimental Design and ANOVA: fixed effects, random effects, mixed effects models, interactions;
 Multivariate analyses: multivariate data, multivariate normal distribution, principal components, ordination.
 Cluster Analyses: distance function, UPGMA/average linkage clustering, construction of phylogenetic trees.
 Nonparametric Methods: signedrank test, MannWhitney test, KruskalWallis test, rank correlation.
 Teaching Methods Since the basic principles of probability and statistics are already covered in the Probability and Statistics course in the 4th Semester, this course will proceed straight to the topics mentioned above, focusing on the principles in brief, and illustrating the applications with numerous examples and exercises. In addition, there will also be invited guest lectures on specific topics.
