MAT 240 Module Three Assignment Stevenson PDF

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Week 3 assignment...

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Median Housing Price Prediction Model for D.M. Pan Real Estate Company

Median Housing Price Prediction Model for D.M. Pan Real Estate Company Savannah Stevenson Southern New Hampshire University

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Median Housing Price Prediction Model for D.M. Pan National Real Estate Company Module Two Notes Regression equation in the graph of y=196.29x-159872 Mean listing price for random sample: $189,126.00 Median listing price for random sample: $167,279.00 Standard deviation of the listing price for random sample: $73,734.54 Mean price per square foot: $97.00 Median price per square foot: $93.00 Standard deviation of the price per square foot: $26.18

Regression Equation Y=196.29x-159872 Determine r R= 0.750901 and since the value is close to +1, there is evidence of a strong positive linear relationship between square footage and house sale price. Examine the Slope and Intercepts Slope= $196.29 Meaning that on average, every additional square foot a house goes up, the price will increase by this amount Intercept= -$159, 872 and since there is no data for square footage that is close to 0, there is no meaningful interpretation of the intercept in this case We are unable to use this data set to determine the value of the land only because the data provided is a listing of home sales which does not correlate to land only sales.

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Median Housing Price Prediction Model for D.M. Pan National Real Estate Company

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R-squared Coefficient The R-squared coefficient is 0.563853 This means that 56% of the variation in the price from the data can be explained by the variation in the square footage. Conclusions The mean square footage for the dataset chosen in the East North Central Region is slightly lower than the national average at 1778 square feet compared to 1944 square feet nationally. The median square footage is also lower for the dataset at 1724 compared to 1901 nationally. For every 100 square foot the price goes up $19,629. This can be determined by multiplying the slope by 100 because the slope shows the average increased price per 1 square foot increase. Based on the regression equation in the graph of y=196.29x-159872 a 1200 square foot house would list for a minimum of $75,676. The regression equation can help identify appropriate listing prices by interpreting the areas correct listing price by using the data. Since the dataset for East North Central Region homes are from 1434-2641 the best range for this graph would be 1400-2700. A box & whisker plot can be used to clearly show the mean and median square footage for the data....