1. Real Estate Sales Forecasting Regression Model of Pueblo neighborhood --------North Elizabeth Data sources from Pueblo County Website

2. I select 5 variables. Dependent variable—price. Independent variable—Bathroom, Bedroom, Lot(sqft), Sqft. PriceBathroomBedroomLot(sqft)Sqft 67715127406754 793321250601282 86354128150816 1772682375602580 1027492457371758 810151465281456 1897823384003148 4037611625618 880821445281956 15177243105603197 52918126250709 514441384001064 792231376801133 1022552360001210 48735128150756 10311723105601414 10285423123751216 569341455201270 2189433387363056

3. Run the model and from the results we can see that: “Sqft” is the most significant one than others. Select “Sqft” as the only independent variable SUMMARY OUTPUT Regression Statistics Multiple R0.930235189 R Square0.865337507 Adjusted R Square0.826862509 Standard Error21051.29835 Observations19 ANOVA dfSSMSF Significan ce F Regression43.99E+109.97E+0922.49095.67E-06 Residual146.2E+094.43E+08 Total184.61E+10 Coefficients Standard Errort StatP-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept19319.6112120802.240.9287270.368767-25296.863935.98-25296.863935.98 Bathroom-1436.07074812878.66-0.111510.912797-2905826185.9-2905826185.9 Bedroom-9849.2038457221.99-1.363780.194164-25338.85640.425-25338.85640.425 Lot(sqft)3.0687116352.3781541.2903750.217826-2.031928.169346-2.031928.169346 Sqft56.6800912513.360084.2424980.0008228.0255885.334628.0255885.3346

4. When throw up other variables just use “Sqft”, the significant F is higher and significant F is lower. The model is more significant. The t test of “Sqft” is higher, P-value is lower. R square almost has no change. This regression model is better than before. The coefficient of x is 54.18, the intercept is 15169.41. So the regression model is y= 54.18x+15169.41. SUMMARY OUTPUT Regression Statistics Multiple R0.910063 R Square0.828214 Adjusted R Square0.818109 Standard Error21576.9 Observations19 ANOVA dfSSMSF Significanc e F Regression13.82E+10 81.960356.52E-08 Residual177.91E+094.66E+08 Total184.61E+10 Coefficient s Standard Errort StatP-valueLower 95%Upper 95% Lower 95.0% Upper 95.0% Intercept15169.4110499.171.444820.166689-6981.937320.73-6981.937320.73 Sqft54.184645.985149.0531956.52E-0841.557166.8121841.557166.81218

5. Significance Testing and residual analysis α=0.05, α/2=0.025, t(0.025,17)=2.11, observed t is 9.05, 2.11<9.05, this regression model is adding significantly more predictive information to the no regression model. Because there are small sample sizes, so it has more inaccuracy than large sample sizes. Look at the residual plot, it seems like relatively linear The variances of the errors are about equal for each value of x, the error terms do not seems to be related to adjacent terms. Look at the normal probability plot, it indicates that the residuals are normally distributed. And this normal plot is relatively close to being a straight line, showing that the residuals are nearly normal in shape.

6. Model Application This model is about the relationship between “price” and “Sqft”. It shows that these two things has a positive linear relationship, which means, when “Sqft” increase, the “Price” will increase. When “Sqft” goes down, the “Price” will decrease to some extent. As a whole, if someone going to sell his house, and if the house is a big one (2000 sqft), then the selling price will be higher than a small one(1000 sqft). USING: When a hose is 2980 sqft, the forecast selling price is y=54.18*2980+15169.41=176625.81.