DataCamp
Course Outline

Modeling an interaction

100xp

In this exercise you will use interactions to model the effect of gender and gastric activity on alcohol metabolism.

The data frame alcohol has columns:

  • Metabol: the alcohol metabolism rate
  • Gastric: the rate of gastric alcohol dehydrogenase activity
  • Sex: the sex of the drinker (Male or Female)

In the video, we fit three models to the alcohol data:

  • one with only additive (main effect) terms : Metabol ~ Gastric + Sex
  • two models, each with interactions between gastric activity and sex

We saw that one of the models with interaction terms had a better R-squared than the additive model, suggesting that using interaction terms gives a better fit. In this exercise we will compare the R-squared of one of the interaction models to the main-effects-only model.

Recall that the operator : designates the interaction between two variables. The operator * designates the interaction between the two variables, plus the main effects.

x*y = x + y + x:y

Instructions

The data frame alcohol is in your workspace.

  • Write a formula that expresses Metabol as a function of Gastric and Sex with no interactions.
    • Assign the formula to the variable fmla_add and print it.
  • Write a formula that expresses Metabol as a function of the interaction between Gastric and Sex.
    • Add Gastric as a main effect, but not Sex.
    • Assign the formula to the variable fmla_interaction and print it.
  • Fit a linear model with only main effects: model_add to the data.
  • Fit a linear model with the interaction: model_interaction to the data.
  • Call summary() on both models. Which has a better R-squared?
    Take Hint (-30xp)