MAT 240 Module Two Assignment Final
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Southern New Hampshire University *
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Feb 20, 2024
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Selling Price 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
Melanie Maxwell
Department of Mathematics, SNHU
MAT 240: Applied Statistics
Dr. Reel
1/21/2024
Selling Price Analysis for D.M. Pan National Real Estate Company
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Introduction
The purpose of this report is to examine the relationship between the selling price of properties and their size in square feet. By using a simple random sampling of 30 properties I will provide a
Selling Price Analysis.
Representative Data Sample
For intents and purposes of this analysis, I have selected the Pacific region. To get a true random sample, I removed all other regions so that the Pacific Region information was the only data left on the spreadsheet. I then entered the formula =RAND(), to generate a random sample. The mean, median, and standard deviation of the list price are 446,630, 405,400 and 128691. The mean, median and standard deviation of the square footage are 1,988, 1,754 and 865.27. The x-
variable of the scatterplot represents the median square footage. The y-variable in the scatterplot represents media listing price.
Data Analysis
The Pacific regional sample created is a reflection of the national market
. While comparing and contrasting my regional sample with the National Market data, I learned that my random sample stats are significantly higher than the National Market. I’ve concluded that this could possibly be explained by the location, as the square footage of the properties were lower than the National Market.
To provide visual representation of the random sample, I created a scatterplot in Excel.
Selling Price Analysis for D.M. Pan National Real Estate Company
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Scatterplot
1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 - 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 f(x) = 143.16 x + 162085.21
Pacific Region
Square Footage
Listing Price
The Pattern
The square footage lies on the x-axis as it is the independent variable. The list price lies on the y-
axis and it is dependent upon the square footage of the property. Though there is a cluster, there is a trend. There is a positive trend, it’s not perfectly linear but it does slightly increase and there is a big hike as the square footage gets higher. There is one unexplained outlier on the graph, it rests at about 3500sq ft. It can only be attributed to the location. Based on the regression line, if I
had a 1200 square foot house I would list it at 333,877 which in my personal opinion is way to much for such a small property.
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