MAT 240 Module Three Assignment Template

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Housing Price Prediction Model for D.M. Pan Real Estate Company Madison Jones Southern New Hampshire University

Median Housing Price Prediction Model for D.M. Pan National Real Estate Company 2 Module Two Notes Mean Median Standard Deviation Square Feet (X) 2,059 1,797 978.0557 Listing Price (Y) 350,157 311,050 125,426.10 Regression Equation The regression equation for the given sample scatterplot developed in Module Two assignment is, y = 123.98x + 94824. Determine r Based on the sample dataset the correlation coefficient or R is determined to be 0.966795824. The correlation coefficient indicates a strong relationship between house square 0 1,000 2,000 3,000 4,000 5,000 6,000 0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 f(x) = 123.98 x + 94823.74 Northeast Region Listing Price Compared to Square Feet Mean Sqft Median Sqft Std Dev Sqft 0 500 1,000 1,500 2,000 2,500 Sample vs National Square Footage Sample National

Median Housing Price Prediction Model for D.M. Pan National Real Estate Company 3 footage and listing price. This was determined by comparing the correlation coefficient to the below table: Value of | R | Strength of correlation 0<| R |≤0.40 Weak 0.40<| R | ≤0.80 Moderate 0.80<| R | ≤1.00 Strong When the given correlation coefficient of 0.966795824 is substituted in to the equation for a strong correlation it is greater than 0.80 hence a strong correlation. It can also be noted that the correlation coefficient is a positive number indicating the direction of the dataset to be positive. This can be confirmed assessing the associated of square footage to listing price. As the square footage of a home increased so does the listing price, again showing the positive relationship between the variables. Examine the Slope and Intercepts Slope: 123.98 Based on the given slope it can be concluded that for everyone square foot increase to a home the listing price should increase $123.98. For example, if a home with 1,658 square feet is listed for $259,300 and the homeowner creates an addition to the home increasing the square footage by 100 square feet the following equation could be used to determine the homes new listing price: 123.98 * 100 = 12,398; 259,300 + 12,398 = 271,698. Intercept= 94824 Based on the given intercept indicating that when the square footage of a home is 0 the listing price should be $94,824. The price of $94,824 can be interpreted as the listing price for just land. Although the intercept is a practical number for just land no data points in the sample

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Median Housing Price Prediction Model for D.M. Pan National Real Estate Company 4 extent that far in the dataset. Hence drawing conclusions based solely on the intercept are no reliable. R -squared Coefficient R-squared is a numerical statistic that indicates how much the variation of Y can be explained by the variation in X . In this analysis R-squared represents how much the variation in square footage explain the variation in listing price. Based on the correlation coefficient R- squared is determined to be 0.9347 indicating 93.47% of the variation in listing price can be explained by the variation in square feet. Conclusions For this analysis a sample of 30 was taken from the Northeastern Region. In Module Two analysis the mean, median, and standard deviation of listing price and square feet of the sample were compared to the national statistics. When comparing the two it is evident that the mean, median, and standard deviation are very similar for both variables. This indicates that the sample from the Northeastern Region is representative of the national association between square footage and listing price. Furthermore, based on the regression line in the dataset it would be appropriate to use the regression equation to identify potential listing prices of homes ranging from 1,100-5,300 square feet. The regression equation identified in this dataset is an appropriate tool in determining the potential listing price of a home based on the square footage. It should also be kept in mind that many factor may influence listing price thus the regression equation is a mere prediction and can be potentially inaccurate of the actual price a home can be listed for.

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