MAT 240 Module Two Assignment
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Southern New Hampshire University *
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MAT240
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Feb 20, 2024
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Selling Price and Area Analysis for D.M. Pan National Real Estate Company
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Report: Selling Price and Area Analysis for D.M. Pan National Real Estate Company
Brittany Campbell
Department of Mathematics, SNHU
MAT 240: Applied Statistics
Dustin Atkinson
Due January 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
The purpose of this report is to determine the listing prices, cost per square foot, and the square footage of homes in the Mountain region of the United States. It is also to gauge the costs of these homes against the National Average cost of homes with similar square footage. This will
help to list homes on the market given the square footage and what the average costs are currently in this area. Generate a Representative Sample of the Data
The region that I chose was the Mountain Region. The mean for the listing prices of my random sample is $345,287, the median is $350,550 and the standard deviation is $48,244. The cost per square foot mean is $184, the median is $179, and the standard deviation is $27. The mean for the square footage is 1,903, the median is 1,904 and the standard deviation is 314. Analyze Your Sample
First, I will explain how I got my random sample of 30. Using the data that was provided I took all the data for the Mountain region and created its own spreadsheet. I then used the =RAND () function to determine the random numbers. By doing this I sorted them using the data
collected in the random column and then took the first set of 30 from that data and had my sample. When looking at the National Averages I found that my sample was higher than the National Average in several points, however, it appeared to be lower in terms of the standard deviations. Generate Scatterplot
Selling Price and Area Analysis for D.M. Pan National Real Estate Company
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Observe Patterns
In this scatterplot, the X variable represents the square footage of homes in the sample and is the most useful when making predictions on selling homes in this market. The Y variable represents the listing price of homes in the sample in the Mountain Region. The association that I can see is that the square feet there are then the higher the price of the home.
The shape I see is linear. If we had an 1,800-square-foot house, based on the regression equation in the graph, then the listing price of the home could easily be priced within the $350,000 range. I did not see any outliers therefore there would not be any data to reflect a reason for their
presence.
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