MAT 240 Module Two Assignment Template
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Selling Price and Area Analysis for D.M. Pan National Real Estate Company
1
Report: Selling Price and Area Analysis for D.M. Pan National Real Estate Company
[Ali Jadoon]
Department of Mathematics, SNHU
MAT 240: Applied Statistics
[Waseem Barham]
[March17
th
,2024]
Selling Price and Area Analysis for D.M. Pan National Real Estate Company
2
Introduction
This report is comprised to analyze the relationship between the selling price of properties in the
United States compared to their size in square feet. With the following information we will be
able to determine the use of square footage as a benchmark for listing prices of homes.
Representative Data Sample
Using the Real Estate Data Spreadsheet, I decided to select the Northeast region which.
condensed the data from 1,000 to 100 samples. To further simplify the sample, I chose 30 of the
100 samples using a randomizing method within the excel spreadsheet. From here I determined
the mean, median, and standard deviation of the listing prices and square foot variables.
The information is as follows:
Sample National
Mean Price
372,667
342,000
Median Price
308,800
318,000
STD Dev Price
158,176
125,914
Sample National
Mean SQFT
2,220
2111
Median SQFT
1,834
1,881
STD Dev SQFT
1159.507 or 1160
921
Data Analysis
Selling Price and Area Analysis for D.M. Pan National Real Estate Company
3
In order to create a truly random sample, I used the formula (=rand ()) on the 100 samples taken
from the Northeast region and input them in the Excel spreadsheet. This generated 100.
samples in a random order in which I selected the top 30. From this I was able to use the
formulas (=average ()), =median ()), and (=STDEV.S()) to determine the mean, median, and
standard deviation of the listing prices (column D) within my sample. I then used the same.
formulas to determine the mean, median, and standard deviation of the square footage of me samples (column F). The data collected shows that the median price, mean SQFT, Standard
deviation price and standard deviation SQFT are higher in the sample compared to the national
averages. While these appear higher, the median price and median SQFT appear lower than the
national averages. Overall, the information pulled from my random sample provide statistics that
are higher than the national market which would mean that the regional sample is not reflective.
of the national market. In the scatterplot above, the Y-axis is representing the listing prices of the houses while the X-
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Selling Price and Area Analysis for D.M. Pan National Real Estate Company
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Axis represents the square feet of the houses in the northeast region. This means that the
dependent variable is the listing price while the independent variable is the square feet. When
considering the two variables, we can see that the variable that is useful in making predictions is
the X-axis or the square feet. This is because there is an association between the listing price of
the houses that rise with the increase of the square footage. Determining the shape of the graph we can see that the general trend was linear with a positive correlation between the two
variables.
If I had a house that was 1,800 square feet, based on the regression equation (y=132.88x+77680)
in the graph, we can determine the ideal listing price to list the house by imputing the square feet
in place of X. The listing price based off the equation (y=132.88(1800) +77680) would be
$316,864.
Reviewing the scatterplot, we can see that the data is well contained considering the use of the
trend line as a base. Points near the far-right area of the scatter plot may be considered outliers
due to them being away from most of the cluster at the far left of the scatter plot. This however is
unlikely due to the few points being relatively close to the trend line. A cause in an outlier may
appear due to the random sampling of the Northeast region in which a house of greater square
footage may not be as common and the appearance of the higher square footage would likely
increase the price of the house significantly, which in turn would create an outlier.