- Fit a model predicting
`log(price)`

from one of the binary variables in the dataset. - Write the linear model, in the form \(\widehat{y} = b_0 + b_1 x\) but using the actual variables instead of \(y\) and \(x\), and using the estimated coefficients using \(b_0\) and \(b_1\).
- Interpret the slope and the intercept.
- Calculate and interpret \(R^2\).

- Fit a model predicting
`log(price)`

from one of the numerical variables in the dataset. - Write the linear model, same as above.
- Interpret the slope and the intercept.
- Calculate and interpret \(R^2\).

- Recreate the recoding of the material variable:
`mat_recode`

(from class) - Fit a model predicting
`log(price)`

from`mat_recode`

. - Write the linear model, same as above.
- Interpret the slopes and the intercept.
- Calculate and interpret \(R^2\).
- Paintings on which material type are predicted to be the most expensive?

At the end write one synthesis paragraph comparing your models and determine which model does the best job in explaining the variability in prices of paintings. Your interpretations should be in context of the data, which means you need to understand the context of your data. Thankfully your data expert will be available to answer questions on Piazza! (But don’t leave them till the last minute.)

Keep interpretations concise!

Codebook: https://stat.duke.edu/courses/Fall15/sta112.01/data/paris_paintings.html

Go to the Resources on Sakai and download

`paris_paintings.csv`

Upload this file to RStudio Server

Load using the following (make sure data file is in the correct working directory):

```
pp <- read.csv("paris_paintings.csv", stringsAsFactors = FALSE) %>%
tbl_df()
```

Your submission should be an R Markdown file in your team App Ex repo, in a folder called `AppEx_04`

.

Thursday, Sep 24, begginning of class

… merge conflics on GitHub – you’re working in the same repo now!

Issues will arise, and that’s fine! Commit and push often, and ask questions when stuck.