Lab 10: Multiple Regression

Learning Objectives

Become comfortable with methods for building and selecting models containing multiple predictor variables using two different data sets:

Mole-rate energy expenditure data:

• We will fit models containing 1 continuous and 1 categorical covariate.
• We will consider an interaction between the continuous and categorical variable.

Longnose dace abundance data

• We will fit a model with multiple predictor variables and use stepAIC for model selection
• We will explore methods for averaging across models

Lab Resources:

• Introductory Presentation
• Detailed Instructions
• R Script Template (.qmd)
  
• Longnosedace Data Set (longnosedace.csv)