---
title: "Lab 5"
author: "Name here"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(warning = FALSE)
library(readr)
library(ggplot2)
library(dplyr)

```
Please use D2L to turn in both the PDF/ Word output and your R Markdown file. For this lab we will work with a dataset containing housing sales from a subset of locations across the United States. There are not any time series elements in this dataset, but rather, the focus will be on regression modeling itself. Additionally, please give more thought to writing in this lab. The dataset can be downloaded from [http://math.montana.edu/ahoegh/teaching/stat408/datasets/HousingSales.csv](http://math.montana.edu/ahoegh/teaching/stat408/datasets/HousingSales.csv).



#### Q1. Housing Sales (100 pts)
This question will focus on a regression framework using a dataset containing housing sales.

##### a. (25 pts)
Download the data, create a figure or two, and discuss what you believe to be important factors pertaining to modeling housing prices.

##### b. (25 pts)
Fit a regression model and write out the mathematical model you are using. Please discuss how you are handling variables and why you chose the final model. (We will cover this in more detail later...)

$$Y_{price} = \beta_0 + \dots ?$$

##### c. (25 pts)
Create a plot of the residuals from your model. 

##### d. (25 pts)
Write a couple paragraph summary of your model. Discuss the implications of your model for housing prices.

