MAT 240 Module Three Assignment
docx
keyboard_arrow_up
School
Southern New Hampshire University *
*We aren’t endorsed by this school
Course
240
Subject
Mathematics
Date
Feb 20, 2024
Type
docx
Pages
3
Uploaded by EarlField10342
Housing Price Prediction Model for D.M. Pan National Real Estate Company
Joshua King
Department of Math, Southern New Hampshire University
MAT 240: Applied Statistics
Daniel Krywaruczenko
1/28/2024
Housing Price Prediction Model for D.M. Pan National Real Estate Company
Regression Equation
Y=106.04x+27181
Determine r
R=0.97
With r being a positive number, it indicates that as square footage increases then the home listing
price increases as well, which means the linear line will be heading upward to the right. Since 0.97 is close to 1, the r value indicates that there is a strong correlation between the two variables
and the association is positive.
Examine the Slope and Intercepts
The slope in this equation is 106.04. The intercept in this equation is 27,181. The slope and intercept in this situation are determining that when the square footage of the home is 0, the listing price should be $27,181. This intercept does not make sense because our data points do not contain any information about land values and a square footage of 0 falls beyond the points of data within our data set even though this value could seem reasonable. The slope and intercept
also tell us that for every increase in 1 square foot, the listing price will change by $106.04. R-squared Coefficient
The r squared value is 0.9327. The context of the r squared value in this scenario represents the amount of the variation in home prices explained by the variance in square footage. It represents there is a 93.2% correlation between the change in house prices versus the change in square footage. Conclusions
Square Footage
National vs. East South
National
East South Central
Central
Mean
2,111
2,410
Std Dev
921
1059.26
Min
1,101
1,607
Q1
1,626
1,947.75
Median
1,881
2,188
Q3
2,215
2,441.75
Max
6,516
6,420
Comparing the data of square footage of homes from the national average and the average within
the east south-central region, the data shows that the east south-central region consists of above average square foot homes compared to the national average.
The slope of the intercept can be used to help identify changes in home prices. By using the calculated slope of 106.04, which represents the price change for every 1 square foot, you can calculate that 106.04*100=$10,604. For every 100 square foot increase, the home price will increase $10,604.
By looking at the scatterplot that was created, all data points lie within roughly 1,600 to 6,500 square feet. Any other square footage below or above this range would be invalid because it is out of range of the calculated data points.
Your preview ends here
Eager to read complete document? Join bartleby learn and gain access to the full version
- Access to all documents
- Unlimited textbook solutions
- 24/7 expert homework help