Data is in the Excel spreadsheet ProjectData.xlsx

Descriptive Abstract:

This dataset comprises $104$ homes.

Variable Descriptions:

housenum: unique house number

bedrooms: number of bedrooms

bikescore: bike friendliness $(0-100$ score, higher scores are better$)$

garage$\_$spaces: number of garage spaces $(0-4)$

distance: distance $($in feet$)$ to the nearest entry point to the rail trail network

no$\_$full$\_$baths: number of full baths $($includes shower or bathtub$)$

no$\_$half$\_$baths: number of half paths $($no shower or bathtub$)$

price: Price at which the property closed in $2019($in thousands of dollars$)$

squarefeet: square footage of interior finished space $($in thousands of sf$)$

walkscore: walk friendliness $(0-100$ score, higher scores are better$)$

zip: location $(18892=$ Funkytown$)$

Answer the following questions based on the data. Clearly explain your

procedure$($s$).$

First Step: Convert Zip: Location into a dummy variable with $1$ for Funkytown and $0$

otherwise.

a$)$ We wish to estimate the most that a homeowner should pay to add a bedroom

that will add $500$ square feet to the house.

i$.$ What is the dependent variable?

ii$.$ Use all the remaining variables $($other than the house number$)$ as

independent variables and then run the regression. Using the results of

your regression, what is the added value of an additional bedroom that

adds $500$ square feet to the house?

b$)$ Using the results of your regression, what is the extra value of having a house in

Funkytown?

c$)$ Turn in the Excel spreadsheet with your regression results. housenumer garage$\_$spaces no$\_$full$\_$baths walkscore bikescore Price no$\_$half$\_$baths bedrooms squarefeet distance ZIP

$121935210\mathrm{.}729030\mathrm{.}9662\mathrm{.}418893$

$211544204\mathrm{.}171030\mathrm{.}961\mathrm{.}9718893$

$3224666338\mathrm{.}662131\mathrm{.}7250\mathrm{.}04337121218893$

$4114061276\mathrm{.}25131\mathrm{.}7270\mathrm{.}55473484818892$

$5013253169\mathrm{.}173041\mathrm{.}5760\mathrm{.}59659090918893$

$6111236211\mathrm{.}487131\mathrm{.}321\mathrm{.}8818893$

$7018297311\mathrm{.}456031\mathrm{.}2020\mathrm{.}44488636418892$

$8018895377\mathrm{.}857142\mathrm{.}1360\mathrm{.}44488636418892$

$9021538227\mathrm{.}681051\mathrm{.}9181\mathrm{.}9318893$

$1001930224\mathrm{.}366031\mathrm{.}0082\mathrm{.}87303030318893$

$11023546218\mathrm{.}785031\mathrm{.}2960\mathrm{.}75662878818893$

$12212038269\mathrm{.}62031\mathrm{.}4321\mathrm{.}4118893$

$13026879447\mathrm{.}842042\mathrm{.}041\mathrm{.}4215530318892$

$14016585386\mathrm{.}446131\mathrm{.}6040\mathrm{.}24678030318892$

$15026874528\mathrm{.}114042\mathrm{.}2780\mathrm{.}39602272718892$

$16012036221\mathrm{.}22031\mathrm{.}2081\mathrm{.}56723484818893$

$17021737320\mathrm{.}206051\mathrm{.}7441\mathrm{.}67670454518893$

$18011736179\mathrm{.}487031\mathrm{.}0081\mathrm{.}59867424218893$

$19014979243\mathrm{.}639141\mathrm{.}9220\mathrm{.}10303030318892$

$20028094325\mathrm{.}666042\mathrm{.}10\mathrm{.}337518892$

$21122954282\mathrm{.}765041\mathrm{.}664118893$

$22114570198\mathrm{.}686020\mathrm{.}8920\mathrm{.}04204545518893$

$2301329168\mathrm{.}761031\mathrm{.}012\mathrm{.}1518893$

$24011132200\mathrm{.}871131\mathrm{.}2482\mathrm{.}62140151518893$

$2512933237\mathrm{.}206031\mathrm{.}32\mathrm{.}57973484818893$

$2611931205\mathrm{.}77031\mathrm{.}042\mathrm{.}718893$

$27016083287\mathrm{.}489041\mathrm{.}4660\mathrm{.}21673863618892$

$28111828223\mathrm{.}067031\mathrm{.}6361\mathrm{.}9118893$

$29113246237\mathrm{.}594041\mathrm{.}3350\mathrm{.}70852272718893$

$30021432275\mathrm{.}218041\mathrm{.}5122\mathrm{.}01174242418893$

$31121827280\mathrm{.}283041\mathrm{.}7521\mathrm{.}99166666718893$

$32019288237\mathrm{.}789031\mathrm{.}9120\mathrm{.}89204545518892$