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. Note that this is an individual assignment.

Assignment

The models you fit should not be the exact same ones we reviewed in class (but they can share some explanatory variables, just not all).

  1. Fit a linear model for predicting log(price) of paintings based on a single numerical variable.
  • Find the slope and intercept of the model, and interpret each in context of the data.
  • Find and interpret the \(R^2\) of the model.
  1. Fit a linear model for predicting log(price) of paintings based on a single categorical variable with two levels.
  • Find the slope and intercept of the model, and interpret each in context of the data.
  • Find and interpret the \(R^2\) of the model.
  1. Fit a linear model for predicting log(price) of paintings based on a single categorical variable with more than two levels. If there are too many levels, consider collapsing down to a reasonable number first, then fit the model.
  • Find the slopes and intercept of the model, and interpret each in context of the data.
  • Find and interpret the \(R^2\) of the model.
  1. Fit a linear model for predicting log(price) of paintings based on at least two variables (and not a ridiculous amount, be selective!). If some of your categorical variables have too many levels, consider collapsing down to a reasonable number first, then fit the model.
  • Find the slopes and intercept of the model, and interpret each in context of the data.
  • Find and interpret the \(R^2\) of the model.

Grading

Total 20 pts
Questions 1 and 2 4 pts * 2 = 8 pts
Questions 3 and 4 5 pts * 2 = 10 pts
Code style and informatively named code chunks 1 pt
Overall organization 1 pt