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
TESTBANKSNERD
Department of Mathematics, SNHU
MAT 240: Applied Statistics
, 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
In the real estate industry, data analysis plays an important role in obtaining insights that
can help an organization like D.M. Pan Real Estate Company gain a competitive edge.
Therefore, exploring the relationship between various factors, such as the size of a property and
its listing price can be of great help to real estate professionals. This report seeks to investigate
the relationship between the selling price of properties and their size in square feet.
Generate a Representative Sample of the Data
For this analysis, I selected the Mid-Atlantic region from the provided dataset. A simple random
sample of 30 properties was then generated using the RAND () function in Microsoft Excel. This
was done by assigning a random number to each property using the RAND () function. The
dataset was then sorted based on these random numbers, and the first 30 properties were selected
for analysis. The sample is shown below.
Report: Selling Price and Area Analysis for D.M. Pan National Real Estate Company
TESTBANKSNERD
Department of Mathematics, SNHU
MAT 240: Applied Statistics
, 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
In the real estate industry, data analysis plays an important role in obtaining insights that
can help an organization like D.M. Pan Real Estate Company gain a competitive edge.
Therefore, exploring the relationship between various factors, such as the size of a property and
its listing price can be of great help to real estate professionals. This report seeks to investigate
the relationship between the selling price of properties and their size in square feet.
Generate a Representative Sample of the Data
For this analysis, I selected the Mid-Atlantic region from the provided dataset. A simple random
sample of 30 properties was then generated using the RAND () function in Microsoft Excel. This
was done by assigning a random number to each property using the RAND () function. The
dataset was then sorted based on these random numbers, and the first 30 properties were selected
for analysis. The sample is shown below.
