Turn in one copy for each group, both as a word or PDF document and the R Markdown source file. This is due by 10 AM Monday, September 11. Groups *may* be asked to talk about their results in class on Tuesday.

## Lab Overview

For this lab, you will be exploring a data set containing housing sales in King County, Washington (the greater Seattle area). The intent of this lab is to get a feel for some of the basic features in R and explore this data set.

The entire lab will be worth 100 points. Please consider clarity of code and thoughtful writing with an emphasis on concise interpretations as each will be considered when grading labs.

## Questions

Answer the following questions in this R Markdown document. Please include code where necessary.

### 1. Factors driving housing prices.

Download the Seattle Housing dataset, available at: http://math.montana.edu/ahoegh/teaching/stat408/data/SeattleHousing.csv.

`#read.csv( )`

#### a. (5 points)

What format are the following vectors in the housing dataset: price, bedrooms, bathrooms?

#### b. (15 points)

What features in the data set do you think are relevant for determining housing prices? How might each of these influence housing prices?

#### c. (15 points)

Create two figures showing the relationship between at least two variables in the data set with the housing price.

`#plot()`

#### d. (15 points)

Summarize the take away points from your figures.

#### e. (20 points)

Choose two variables to create a subset of the dataset. Then summarize and describe the differences in your selected subset with the data set. This should include some numerical summaries and qualitative descriptions.

### 2. Modeling Housing Prices

#### a. (15 points)

Based on what you have found in this data set, how might you model housing prices (\(Y_{price} = ?\))? Note, I am not asking you to fit a model, but rather describe important relationships between the variables and housing prices. You may discuss statistical modeling techniques, but we will cover these later in the course.

#### b. (15 points)

Suppose you have developed a model to predict housing prices in the King County area, how could these results be applied to the Bozeman housing market?