MAT 240 Module Three Assignment
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Southern New Hampshire University *
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240
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Mathematics
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Feb 20, 2024
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docx
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Housing Price Prediction Model for D.M. Pan National Real Estate Company
Department of Math, Southern New Hampshire University
MAT 240: Applied Statistics
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Housing Price Prediction Model for D.M. Pan National Real Estate Company
Module Two Notes
A random sample of 30 real estate properties was selected, and their median listing price and square footage were recorded for this study. The standard deviation measures the variation in the data from the mean. In this case, the standard deviation for the listing price was $46,116.59, the cost per square foot was $17.85, and the square footage was 445.86. Based on the descriptive statistics, it was evident that the average listing price from our study, which was $258,310, was lower than the national market average of approximately $342,365. An increase in the area of real estate would lead to a corresponding increase in its selling price. The positive regression equation suggests that an increase in the independent variable would result in proportional growth of the dependent variable.
Regression Equation
y = 78.629x + 101010
Determining r
The correlation coefficient equals 0.760183386, indicating a strong correlation in the data. Furthermore, there is a positive correlation between square footage and listing price; as the square footage increases, so does the listing price.
The Slope and Intercepts
The slope of the line indicates that when X reaches zero, Y will be 101,010. This implies that when the property has no square footage, indicating the absence of a built home, the land should be valued at approximately $101,010 in this area, which aligns logically. The intercept signifies that for every 1 unit increase in square footage, the listing price should increase by $78.629.
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R-squared Coefficient
The R-squared value or coefficient of determination is 0.577878781, indicating the extent
to which the listing price is affected by changes in square footage. With a coefficient determination close to 1, we can infer that approximately 58% of the variation in listing price can
be attributed to the differences in the square footage of the homes under consideration.
Conclusions
When comparing the average, middle value, standard deviation, minimum, maximum, and quartiles of the national statistic worksheet to my sample data of the West South-Central region, it is evident that the square footage is consistently higher nationally than in the West South-Central region. However, there is only a slight difference in the first quartile and median values, which are slightly higher in the West South-Central region. Applying a slope, we find that for every additional 100 square feet, the price of a home should increase by $7,862.90. The graph should primarily be utilized for homes with a square footage range of 1,000 to 4,000. Any data beyond this range may not be as reliable since it falls outside the parameters of the regression model.
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