# Introduction & prerequisites

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](part-basic-stats)
- **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](notebooks/mqb-prework-exercises) covering probability distributions, likelihood concepts, and numerical optimisation

## Recommended pre-work

- If you haven’t done it recently: revisit regression + diagnostics in [Basic Data Analyses and Statistics](part-basic-stats).
- If likelihood/probability feels unfamiliar: do the [Pre-work exercises](notebooks/mqb-prework-exercises) before starting.

## Do this first

1. [GLMs](notebooks/glms)
2. [NLLS](notebooks/nlls)
3. [MLE](notebooks/mle)