Quantitative Methods for Life Scientists
Course Notes (2021-22)

Getting started in R

Graphing in R

Probability distributions

         - normality, test statistics, binomial, lognormal, uniform, Poisson, NB

Uncertainty and error bars

         - standard deviation vs. error, confidence intervals, t, nonparametrics

Comparing populations

         - Student’s t, ANOVA, multiple comparisons, post-hoc tests

Linear models: least squares

         - regression tables, collinearity, Type I-III SS, variance inflation, nls

Linear models: likelihood [likelihood summary] [information criteria notes]

         - likelihood functions and profiles, optimization, model comparison, AIC

Generalized linear models [GLM notes]

         - Poisson regression, logistic regression, overdispersion, link functions

Mixed models 1: block models [Mixed model notes]

         - fixed vs. random effects, lme4

Mixed models 2: autocorrelation [correlation structures notes]

         - covariance matrices, semivariograms, correlograms, nlme, Moran’s I

Bayesian models 1: optimization [Bayes notes]

         - JAGS introduction

Bayesian models 2: hierarchical Bayes

         - random intercept and slope models, missing values, observation error