MAT 240 Module Two Assignment
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Southern New Hampshire University *
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240
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Mathematics
Date
Apr 3, 2024
Type
docx
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3
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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
Tyler Schmidt
Department of Mathematics, SNHU
MAT 240: Applied Statistics
Professor Childs
1/21/2024
Selling Price and Area Analysis for D.M. Pan National Real Estate Company
2
Report: Selling Price and Area Analysis for D.M. Pan National Real Estate Company
Introduction
This report identifies key data within the housing market of the Northeast. It outlines the relationship between the square footage of properties and their listed prices. Lastly, it will also indicate that best prices for certain square footage of properties with the use of graphs.
Generate a Representative Sample of the Data
The region selected is the Northeast, and a random sample of 30 was picked from that region.
sample
national
mean price
$321,277
342,365
median price
$302,450
318,000
std dev price
109714.9368
125,914
sample
national
mean sqft
1,921 2,111
median sqft
1,698 1,881
std dev sqft
852.5499439
921
Analyze Your Sample
The regional sample is reflective of the national market. This is because the mean, median, and standard deviation of both the price and square feet of the sample and national market are quite similar. The only difference seen is that the regional sample has a bit smaller price, but this is most likely since the size of the properties is also a bit smaller. The sample of 30 is random because the =rand() function was used on Excel to give each property a random number. The numbers were then sorted in order and the first 30 were picked.
Selling Price and Area Analysis for D.M. Pan National Real Estate Company
3
Generate Scatterplot
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
Observe Patterns
X = square feet of the properties within the sample of 30.
Y= listing prices in dollars of the properties within the sample of 30.
The x variable is useful for making predictions considering it is the independent variable out of the two.
The association between x and y is they both are linear. This means that if x is a higher value y typically reflects that and will also be a higher value.
The shape of the scatterplot is linear.
If you had an 1,800 square foot house, based on the regression equation in the graph, I would list it for $144,249.
If there were any outliers in the plot it would be the 4 above $500,000.
These outliers likely appeared because they are far out of the range of the average square feet. So, the price must reflect that sheer difference in size. These outliers represent the larger homes in the Northeast.
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