Biology 708, McMaster University

Introduction

Linear models are a kind of lamppost: a very powerful set of tools for solving certain kinds of problems, using certain kinds of assumptions.

Class materials

• Main lecture notes
  • slide version
• Crabs/shrimp example
  • Crabs/shrimp data
• Parameters

Resources

• Introductory
  • The model section from the R intro book
  • Fridley examples and guidelines
  • Rodriguez introduction
  • Bolker book (see Sec. 9.2)
  • Faraway linear models book draft
    • Book home page
  • Hector et al on ANOVA for unbalanced data (i.e. “type III sums of squares” revisited)
  • Schielzeth (2010) on centering and scaling in linear models
  • Murtaugh (2007) on averaging in analysis of nested designs
• Discussion
  • Hurlbert’s classic Pseudo-replication paper
  • Exegeses on linear models (advanced)

Assignment

• Formulate a clear hypothesis about your data.
• Make a linear model for your hypothesis.
• Draw and discuss at least one of each of the following:
  • diagnostic plot
  • prediction plot
  • inferential plot (e.g., a coefficient plot, or something from emmeans or effects)