Please use D2L to turn in both the PDF or Word output and your R Markdown file in.
Explore the Seattle Housing dataset graphically and choose a metric variable to use to model housing prices.
Seattle <- read.csv('http://math.montana.edu/ahoegh/teaching/stat408/datasets/SeattleHousing.csv',
stringsAsFactors = F)
str(Seattle)## 'data.frame': 869 obs. of 14 variables:
## $ price : num 1350000 228000 289000 720000 247500 ...
## $ bedrooms : int 3 3 3 4 3 3 4 5 3 2 ...
## $ bathrooms : num 2.5 1 1.75 2.5 1.75 2.5 1 2 2.5 1 ...
## $ sqft_living : int 2753 1190 1260 3450 1960 2070 2550 2260 1910 1000 ...
## $ sqft_lot : int 65005 9199 8400 39683 15681 13241 4000 12500 66211 10200 ...
## $ floors : num 1 1 1 2 1 1.5 2 1 2 1 ...
## $ waterfront : int 1 0 0 0 0 0 0 0 0 0 ...
## $ sqft_above : int 2165 1190 1260 3450 1960 1270 2370 1130 1910 1000 ...
## $ sqft_basement: int 588 0 0 0 0 800 180 1130 0 0 ...
## $ zipcode : int 98070 98148 98148 98010 98032 98102 98109 98032 98024 98024 ...
## $ lat : num 47.4 47.4 47.4 47.3 47.4 ...
## $ long : num -122 -122 -122 -122 -122 ...
## $ yr_sold : int 2015 2014 2014 2015 2015 2014 2014 2014 2015 2014 ...
## $ mn_sold : int 3 9 8 3 3 6 6 10 1 11 ...Fit a t-distribution regression model for housing price. Specify the sampling model and all necessary prior distributions.
Summarize the results from this model.