University Park Real Estate Analysis Real Estate & Homes For Sale - Zillow. (n.d.). Retrieved December 2, 2014, from

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University Park Real Estate Analysis Real Estate & Homes For Sale - Zillow. (n.d.). Retrieved December 2, 2014, from , , _rect/15_zhttp:// , , _rect/15_z Cooper Hoag BUSAD 360

Sample Data Set AddressBedroomsTotal SqFtSelling PriceGarageGarage SqFtDetatchedAttachedNoneBathLot SqFt 4331 Fireweed Dr $198,000Attatched Scarlet Sage Dr Attached Terrace Dr attatched Erica Ct Attached Woodsorrel Ct Attached Gleneagle Ct Attached Ridgewood Ct Attached Mayweed Ct Attached Scarlet Sage Dr Attached Cornflower Ct Attached Cornflower Ct Attached Desert Candle Dr Attached Ironweed Dr Attached Bayweed Ct Attached Cornflower Ct Attched

Regression Analysis (Model Runs, Variable Selection) SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations15 ANOVA dfSSMSFSignificance F Regression E-05 Residual Total Coefficien ts Standard Errort StatP-valueLower 95% Intercept Bedrooms Selling Price Garage SqFt Bath Lot SqFt SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations15 ANOVA dfSSMSFSignificance F Regression Residual Total Coefficients Standard Errort StatP-valueLower 95% Intercept Garage SqFt Lot SqFt

Description of Final Model In the original model, the f-stat being at is very average, r- squared is related to the model, p-value shows that the different variables matter, t-stat signal that beds, lot sqft, and garage sqft are the most related and most important. The second model for r-squared at.67 is average and the low f probably means that it was luck or chance that it was this low. T-stat for lot sqft is more reliable. The P-stat doesn’t say anything significant but says that the variables are relivent.

Residual Analysis I do believe I messed up with creating my equation or at least using the wrong kind of variables but this graph is comparison of Selling Price (orange) and Predicted Selling Price (blue). The formula I used for predicting selling price is under the chart. Y^= *total sqft *number of bedrooms+3.7*lot sqft

Model Application AddressTotal SqFtBedroomsSelling PriceGarage SqFtLot SqFtBathGarageDetatchedNoneAttached 4331 Fireweed Dr $198, Attatched Scarlet Sage Dr Attached001 With the first house the predicted sales come out to be $ and the second comes out to be $ These prices are far from a realistic prediction. I became lost on which variables to use so I just used ones that I thought were the most relevant to what consumers are looking for. Obviously that wasn’t the correct way to go about it but other variables had even more drastic outcomes so I stuck to this way.