# import the necessary modules

house $=$ # read in the csv file

y$,$ X $=$ # create matrices y$,$ from the price column, and X$,$ from the bedrooms, bathrooms, sqft$\_$living, sqft$\_$lot, yr$\_$built, and sqft$\_$living$15$ columns

vif $=$ # calculate the VIF for each X$\_$i

result $=[$round$($num$,6)$ for num in vif$]$

print$($result$)2\mathrm{.}11$ LAB: Calculating VIF using variance$\_$inflation$\_$factor$()$

The kc$\_$house$\_$data dataset contains information on house sale prices in King County, Washington from May $2014$ and May $2015.$ The

columns include sale price, and a number of variables that might affect the price.

Load the data set into a data frame.

Create matrices $y,$ from the price column, and $x_{,}$ from the bedrooms, bathrooms, sqft$\_$living, sqft$\_$lot, yr$\_$built, and sqft$\_$living$15$

columns

calculate the VIF for each predictor variable.

Ex: If sqft$\_$lot$15$ is used instead of sqft$\_$living$15$ the output is:

$1\mathrm{.}563916,2\mathrm{.}961241,2\mathrm{.}742922,2\mathrm{.}076869,1\mathrm{.}382412,2\mathrm{.}094995$