Description
The file Lab 9 – Real Estate Data.csv contains real estate transaction data from
2014 – 2015. There are 21,597 tuples of data, with each attribute described in the
file. The task is to determine a multi-linear regression relationship of the form,
x1 + x2 · Bedrooms + x3 · Bathrooms + x4 · Floors + x5 · W aterfront
+ x6 · Condition + x7 · Grade + x8 · Y earBuilt
+ x9 · LivingSpace + x10 · LotSize = P rice.
Set up and solve the multi-linear regression system, then report the solution.
(1) Import the data from Lab 9 – Real Estate Data.csv.
(2) Create the multi-linear regression system, i.e. determine A and b.
(3) Solve for xˆ = (x1, x2, . . . , x10), the vector which minimizes ||Axˆ − b||2
.
(4) Report the least-squares solution xˆ.
Use this model to predict the selling price for the real estate transaction with the
following data. The predicted price you should get is $2,608,060.01.
Price = $5,300,000
Bedrooms = 6
Bathrooms = 6
Floors = 2
Waterfront = Yes
Condition = 4
Grade = 12
Year Built = 1991
Living Space = 4,320 sq. ft.
Lot Size = 24,619 sq. ft.
Lastly, determine which real estate transaction has a predicted selling price closest
to the actual selling price and list the attribute data.
Submit a diary file (.txt format) with all MATLAB work shown. Make sure to
suppress output where appropriate.