Compare and contrast your sample with the population using the National Statistics and Graphs document.

Scenario:
Smart businesses in all industries
use data to provide an intuitive analysis of how they can get a competitive
advantage. The real estate industry heavily uses linear regression to estimate
home prices, as cost of housing is currently the largest expense for most
families. Additionally, in order to help new homeowners and home sellers with
important decisions, real estate professionals need to go beyond showing
property inventory. They need to be well versed in the relationship between
price, square footage, build year, location, and so many other factors that can
help predict the business environment and provide the best advice to their
clients.

Prompt
You have been recently hired as a
junior analyst by D.M. Pan Real Estate Company. The sales team has tasked you
with preparing a report that examines the relationship between the selling
price of properties and their size in square feet. You have been provided with
a Real Estate Data spreadsheet that includes properties sold nationwide in
recent years. The team has asked you to select a region, complete an initial
analysis, and provide the report to the team.
Note: In the report you prepare for the sales team, the
response variable (y) should be the listing price and the predictor variable
(x) should be the square feet.
Specifically you must address the following rubric
criteria, using the Module Two Assignment Template (attached)
·
Generate a Representative Sample of the Data
·
Select a region and generate a simple random
sample of 30 from the data.
·
Report the mean, median, and standard deviation
of the listing price and the square foot variables.
·
Analyze Your Sample
·
Discuss how the regional sample created is or is
not reflective of the national market.
·
Compare and contrast your sample with the
population using the National Statistics and Graphs document.
·
Explain how you have made sure that the sample
is random.
·
Explain your methods to get a truly random
sample.
·
Generate Scatterplot
·
Create a scatterplot of the x and y variables
noted above and include a trend line and the regression equation
·
Observe patterns
·
Answer the following questions based on the
scatterplot:
o
Define x and y. Which variable is useful for
making predictions?
o
Is there an association between x and y?
Describe the association you see in the scatter plot.
o
What do you see as the shape (linear or
nonlinear)?
o
If you had a 1,800 square foot house, based on
the regression equation in the graph, what price would you choose to list at?
o
Do you see any potential outliers in the
scatterplot?
o
Why do you think the outliers appeared in the
scatterplot you generated?
o
What do they represent?

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