Advanced Data Analyses and Statistics

Advanced Data Analyses and Statistics#

This part builds toward modern statistical modelling and inference workflows used in quantitative biology.

Learning goals#

By the end of this part, you will be able to:

  • Extend linear models to non-Gaussian responses via generalised linear models (GLMs)

  • Fit and interpret logistic regression (binary) and Poisson regression (count data) models

  • Recognise and handle temporal autocorrelation in time-series data

  • Fit nonlinear models using nonlinear least squares (NLLS) with appropriate starting values

  • Construct likelihood functions and estimate parameters via maximum likelihood estimation (MLE)

  • Compare models using information criteria (AIC, BIC) and likelihood ratio tests

  • Apply modern statistical workflows for model fitting, checking, and inference in quantitative biology

Prerequisites#

  • Essential: Solid foundation in linear regression and model diagnostics from Basic Data Analyses and Statistics

  • Essential: Comfort with R programming, model formulas, and interpreting model output

  • Strongly recommended: Complete the basic statistics part first — these chapters build directly on regression concepts

  • Helpful refresher: Pre-work exercises covering probability distributions, likelihood concepts, and numerical optimisation

Do this first#

  1. GLMs

  2. NLLS

  3. MLE

Contents