Overview

This week, you’ll learn about statistical models, which are mathematical representations of data relationships. Specifically, you’ll explore the general linear model (GLM), a broad framework for analyzing data patterns.

Your first GLM will be simple linear regression, which fits a straight line to data to predict a response variable (outcome) based on an explanatory variable (predictor). We’ll examine the two key parameters estimated in this model: the slope (which shows how the predictor influences the outcome) and the intercept (the value when the predictor is zero). We’ll also assess whether these values are significantly different from zero.

Learning objectives

The successful student will be able to:

  • explain what is meant by a statistical model and fitting a model

  • know what the general linear model is and how it relates to regression

  • explain the principle of regression and know when it can be applied

  • apply and interpret a simple linear regression in R

  • evaluate whether the assumptions of regression are met

  • scientifically report a regression result including appropriate figures

Instructions

  1. Prepare

    1. 📖 Read What is a statistical model
    2. 📖 Read Single linear regression

  2. Workshop

    i.💻 Carry out a single linear regression

  3. Consolidate

    1. 💻 Appropriately analyse the relationship between juvenile hormone and mandible size in stage beetles
    2. 💻 Appropriately analyse the relationship between anxiety and performance