The logic behind multiple linear regression is similar to that of simple regression. This week we will look at what happens when you regress more than one covariate against Y.
We will focus on running and interpreting these models, but will not look at assessing their fit just yet. (We will save that for Week 5).
- The basic structure of a general linear model
- How to interpret beta values for categorical and continuous covariates
- Interation terms
- Specifying models in lm()
Part 1 will involve some relatively simple code, but perhaps complicated concepts. Part 2 will be more hands-on, and there will be a larger exercise as we work on the data from the lionfish paper below.
In Part 1, we will look at urchin data from the paper we worked on in week 2. In Part 2, we will use data from the paper below:
Peiffer F, Bejarano S, Palavicini de Witte G, Wild C. (2017) Ongoing removals of invasive lionfish in Honduras and their effect on native Caribbean prey fishes. PeerJ 5:e3818 https://doi.org/10.7717/peerj.3818
You may read if interested.
Part 1 slides via speakerdeck
Part 2 slides via speakerdeck