MAT 240 Module Three Assignment
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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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3
Uploaded by AmbassadorSparrowPerson1076
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Housing Price Prediction Model for D.M. Pan National Real Estate Company
Tyler Schmidt
Department of Math, Southern New Hampshire University
MAT 240: Applied Statistics
Professor Childs
1/28/2024
2
Housing Price Prediction Model for D.M. Pan National Real Estate Company
Module Two Notes
500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 - 100,000 200,000 300,000 400,000 500,000 600,000 700,000 f(x) = 125.1 x + 80931.02
House Prices in the Northeast
square feet
listing prices
Regression Equation
y = 125.1x + 80931
Determine r
r = 0.97211525
r is the correlation between the listing price and the square feet of this data set. It is apparent that r is positive meaning it is moving in an upwards direction and the correlation between x and y is very strong considering r is very close to 1.
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Examine the Slope and Intercepts
Slope = 125.1
Intercept = 80931
After examining both the slope and the intercept it becomes obvious that the intercept does not make sense and is not very useful. This is because the intercept represents what the listing price of a home of 0 square feet should be based on the regression equation. This does not make sense mainly because if a home or property is 0 square feet you are not purchasing anything, and this does not represent the real world in the slightest.
R-squared Coefficient
In this analysis R-squared represents how much variation within listing prices is explained by the variation in square feet. Within this analysis R-squared = 0.945008059. Which means that roughly 95% of the variation within the listing prices is explained by the variation in square feet, which is quite good.
Conclusions
The square footage of homes in my selected region is seen to be consistently lower than the national statistics in the United States. The only outlier of this is the national min is lower than my selected region.
For every 100 square feet the listing price goes up by roughly $12,510 in my selected region.
This scatterplot graph would be best used for square footage between 1,150 and 4,700 because most of the data falls between this range. So, if any data was inputted outside of this range the accuracy would not be dependable.
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