Task
Your goal is to explore several models between various variables in the dataset and price of paintings. For this application exercise you are asked to stick to models with one explanatory variable.
- Model fitting:
- Try at least 5 different linear models for painting price using at least 5 different explanatory variables.
- Make sure some are categorical and some are numerical.
- Extra credit: For the adventorous… Re-level at least one of your variables, e.g. combine a few levels into one, or turn a numerical variable into a categorical variable. When doing this, do not overwrite the original variable, but instead create a new variable. Hint: the
forcats package might be useful for working with categorical variables but there are other ways of accomplishing this too.
- Interpretations:
- Interpret the slope and the intercept of each model.
- Interpret the \(R^2\) of each model.
Synthesis:
- Write one paragraph synthesizing and comparing your models and determine which model you think does the best job in explaining the variability in prices of paintings. Does your choice make sense in the context of the data? Explain.
Submission instructions
There should be a AppEx_09_29_2016.Rmd file in your Teams AppEx repo, add you answers and writeup to that file and commit and push your changes to github.
Due date
Thursday, Sep 29, by 8 pm