Accessing the data

The data can be found in the \data folder of your homework repository.

Read the data with

pp <- read_csv("data/paris_paintings.csv")

Take a peek at the codebook.

Accessing the assignment repo

Go to the #assignment-links channel on Slack and click on the link for mini-hw-07, and accept the assignment.

Task

In this case study your goal is to fit models that include both main and interaction effects and do model selection.

Planning:

  1. Decide on a subset of variables to consider for your analysis. Think about it as focusing on certain aspects of the price determination, as opposed to all aspects. You’re allowed a maximum of 10 total variables to consider, including interactions. The more variables you consider the longer model selection will take so keep that in mind.

  2. Decide among these which variables might make sense to interact. Remember, we consider interactions when variables might behave differently for various levels of another variable. Ideally, you would get some expert guidance for choosing which variables to interact, however the curators of the data are not in class with you, so instead they have compiled a list of interactions that might be potentially interesting:

    • School of painting & landscape variables: school_pntg & landsALL / lands_figs / lands_ment
    • Landscapes & paired paintings: landsALL / lands_figs / lands_ment & paired
    • Other artists & paired paintings: othartist & paired
    • Size & paired paintings: surface & paired
    • Size & figures: surface & figures / nfigures
    • Dealer & previous owner: dealer & prevcoll
    • Winning bidder & prevcoll: endbuyer & prevcoll
    • Winning bidder & artist living: winningbiddertype & artistliving

This is not an exhaustive list, so you might come up with others.

Model fitting and selection:

  1. Use backwards elimination to do model selection. Make sure to show each step of decision (though you don’t have to interpret the models at each stage).

Model interpretation:

  1. Provide interpretations for the slopes for the final model you arrive at and create at least one visualization that supports your narrative.

Grading

Check / no check