Waveney DIY Centers $($WDC$)$ operates a few dozen stores in the eastern United States. The stores are popular with home remodelers, contractors, and do$-$it$-$yourself customers. The managers at Waveney are interested in understanding what drives costs as well as getting better cost estimates when planning new stores. The area manager for the Southeast Region is interested in new data analysis approaches to management and offered to run a test using data from the $14$ stores in the region.

The initial thoughts of the managers and the financial analysts in the region were that two primary factors drove store costs: store area $($square footage$)$ and revenue. The following data were collected from the most recent year of operations $($revenues and costs in thousands of dollars$).$

Store Revenues

$($$$000)$ Area

$($Square Feet$)$ Costs

$($$$000)$

SE$-01$ $ $21,04569,647$ $ $16,299$

SE$-0215,98251,91712,995$

SE$-0326,22183,83119,710$

SE$-0419,88967,87415,579$

SE$-0520,16659,00915,612$

SE$-0618,25667,87414,599$

SE$-0725,00596,24819,191$

SE$-0813,20146,59811,247$

SE$-0923,58178,51217,953$

SE$-1016,33062,55513,364$

SE$-1120,85866,10116,133$

SE$-1217,62073,19314,297$

SE$-1314,17462,55512,070$

SE$-1423,40474,96617,794$

Results from a regression of store costs on store area are as follows.

Equation:

Store costs $=$ $$3,275\mathrm{.}02($in thousands$)+($$$0\mathrm{.}196\backslash$times Area$)$

Statistical data

Correlation coefficient $0\mathrm{.}863$

R$20\mathrm{.}745$

Required: