MAT 240 Module Three Assignment Template
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Southern New Hampshire University *
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240
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Mathematics
Date
Apr 3, 2024
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docx
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Uploaded by AmbassadorJay2528
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Housing Price Prediction Model for D.M. Pan National Real Estate Company
Briana Burgess
Department of Math, Southern New Hampshire University
MAT 240: Applied Statistics
Gerald Weyand
March 24, 2024
2
Housing Price Prediction Model for D.M. Pan National Real Estate Company
Module Two Notes - The Pattern
The report aims to offer valuable information that can assist prospective homebuyers, sellers, and real estate agents in understanding the latest market trends for their upcoming activities. The emphasis on square footage as a predictor is because this factor determines the appropriate listing price for a house.
Regression Equation
The most suitable regression equation representing this relationship is y = 122.48x + 25396.
Determine r
The correlation coefficient (r) assesses the relationship between square footage and listing
price, with a value of 0.9394 in this sample size. Consequently, the squared correlation 2,000 1,000 3,000 4,000 5,000 6,000 7,000 - 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 f(x) = 122.48 x + 25395.82
R² = 0.88
West South Central Region
Square foot (X) Listing Price (
Y)
3
coefficient (r^2) equals 0.8825. Given that 0.80 is less than r and r is greater than or equal to 0.1, it indicates a strong correlation between listing price and square footage.
Examine the Slope and Intercepts
The positive slope of $122.48 indicates that as the X variable (square footage) increases, the Y variable (listing price) also increases. The intercept, which is $29,356, signifies the listing price when the square footage is zero. However, since the slope value is greater than 0, the intercept doesn't have a meaningful interpretation in this context. Essentially, for every additional
square footage, the listing price increases by $122.48. Therefore, if the land area is 0 square feet, the estimated listing price would be $29,356.
R-squared Coefficient
The coefficient of determination (R-squared) quantifies the proportion of variability in the dependent variable, which in this case is the listing price, that can be explained by the independent variable, square footage. In the given sample, the R-squared value is 0.8825, indicating that approximately 88.25% of the variability in listing prices can be accounted for by changes in square footage.
Conclusions
The square footage data from my sample closely aligns with the national averages, where
the national mean and median square footage are 2,111 ft and 1,881 square feet, respectively. In comparison, my sample size exhibits a mean of 2,024 ft and a median of 1,852 square feet. This indicates a strong similarity between the broader trends in square footage and the specific observations in my dataset.
The slope of $122.48 in the regression equation reveals how prices change with increasing square footage; specifically, for every additional 100 square feet, the price is expected to rise by $122.48. This insight is valuable for setting appropriate listing prices for current properties and forecasting future property investments.
Based on my dataset, the square footage range of 1,400 to 6,000 square feet is the most suitable for graphing and analysis within the context of this regression equation.
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