MAT 240 Module 3 Assignment

1 Housing Price Prediction Model for D.M. Pan Real Estate Company Housing Price Prediction Model for D.M. Pan Real Estate Company Erika V Estrada Southern New Hampshire University

Median Housing Price Prediction Model for D.M. Pan National Real Estate Company 2 Module Two Notes Region Stat e County Median listing price Median $'s per square foot Median square feet Random Northeas t pa franklin 277,700 $204 1,363 0.99319402 Northeas t pa northumberlan d 633,300 $134 4,736 0.76540629 3 Northeas t ny herkimer 306,900 $171 1,797 0.60495823 2 Northeas t ny cayuga 523,300 $167 3,141 0.04274497 2 Northeas t ny tompkins 238,000 $142 1,678 0.30196386 3 Northeas t ny new york 314,400 $191 1,644 0.39211461 3 Northeas t pa armstrong 289,200 $186 1,558 0.91222742 9 Northeas t pa chester 234,800 $156 1,506 0.51624676 3 Northeas t ny columbia 266,000 $165 1,610 0.00085859 5 Northeas t pa chester 786,800 $149 5,290 0.28174373 1 Northeas t ny albany 297,400 $207 1,439 0.85577632 8 Northeas t ny onondaga 307,300 $179 1,714 0.57694396 1 Northeas t ny chemung 282,600 $228 1,242 0.79240555 2 Northeas t ny tioga 257,400 $218 1,183 0.16612089 5 Northeas t pa bucks 325,800 $182 1,794 0.81693288 9 Northeas t pa washington 315,500 $173 1,820 0.21077264 3 Northeas t pa beaver 315,300 $157 2,010 0.62144908 6 Northeas t pa york 368,600 $178 2,068 0.71365984 9 Northeas t ny steuben 270,600 $191 1,416 0.47503151 5 Northeas t pa erie 229,200 $181 1,263 0.81097507 3 Northeas t ny cattaraugus 585,600 $161 3,637 0.14202016 5

Median Housing Price Prediction Model for D.M. Pan National Real Estate Company 4 Determine r The correlation is determined by [r], which defines the association and strength between two variables. In this case, those variables are the median square feet area and the median listing price. The formula we need to determine [r] is =CORREL () followed by enter. In this case, the value of [r] = 0.97, which means the correlation is strong, and the way we determine this is by understanding that the closer the number is to 1 or -1, the stronger the correlation. The correlation here is positive, and what factors this is that as the square footage increases, the price also increases. For this reason, the association of the two variables, axis X and axis Y, is positive; therefore, the correlation is positive. Examine the Slope and Intercepts Our regression equation shows that the slope b1 is 129.63, and the intercept b0 is 81357. The data we used for this sample shows houses already built; it does not specify any land. If it did, it would reflect on the scatterplot graph. Therefore, trying to give value to a piece of land with no square footage makes no sense. This scatterplot graph shows that when the regression line crosses, it is still on the positive side of the Y-axis. We need to remember that when x=0, the square feet are zero. Nevertheless, the regression equation shows that when x=0 (square feet), y=81357 (intercept). R -squared Coefficient To determine the R-squared r2 coefficient, we must multiply the correlation number by itself. Another method is using the formula =RSQ () followed by enter. Select the known Ys (the listing price) and then known Xs (the square feet), then enter.

Median Housing Price Prediction Model for D.M. Pan National Real Estate Company 5 The coefficient of determination measures the variation between the dependent variable Y-axis, which is affected by the independent variable X-axis. As we have mentioned before, as the square footage increases, the price increases. We want R-squared to be as close to one as possible; in this case, 0.947 or 0.95 means that the variation of the square footage explains 95% of the variation in the Median listing price. Conclusions To conclude, as we already discussed, as the square footage increases, the price also increases, leaving us with a clear understanding that there is a positive relationship between the square feet and the sales price. The following chart compares the National average and the northeast (chosen sample) of the United States. Column1 NATIONAL NORTHEAST REGION N 1000 1,242 MEAN 2111 2,033 MEDIAN 1881 1,627 STD DEV 921 1044.44 MIN 1101 1,183 Q1 1626 1508.75 Q3 2215 1965.5 MAX 6516 5,290 We know the regression equation, slope, and [r] measure any change caused by the independent variable to the dependent variable. In this case, the price also increases as the square feet increase. Referring to the chart, we notice the prices for the Northeast area, in comparison with the national average, have a lower cost per square foot.