Introduction

Modern biological research generates large amounts of data, and making sense of these data requires reliable statistical tools. Linear models are among the most widely used methods for analysing biological data because they help researchers understand how different factors influence an outcome.

In simple terms, linear models allow us to test whether changes in one or more variables, such as treatments, environmental conditions, or genetic differences are associated with measurable changes in biological responses. They help researchers compare groups, evaluate experimental effects, and quantify relationships between variables in a clear and interpretable way.

In this tutorial, you will learn the basic concepts behind linear models and how they are applied in biological research. Through practical exercises using R, you will gain hands-on experience analysing real biological datasets, enabling you to identify patterns, test hypotheses, and interpret results with confidence.

Let’s dive into the practicals on the next page.