ScenarioSmart 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.
PromptYou 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:
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 Summary Statistics and Graphs Real Estate Data document.
Explain how you have made sure that the sample is random.
Explain your methods to get a truly random sample.
Create a scatterplot of the x and y variables noted above and include a trend line and the regression equation
Answer the following questions based on the scatterplot:
Define x and y. Which variable is useful for making predictions?
Is there an association between x and y? Describe the association you see in the scatter plot.
What do you see as the shape (linear or nonlinear)?
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?
Do you see any potential outliers in the scatterplot?
Why do you think the outliers appeared in the scatterplot you generated?
What do they represent?
You can use the following tutorial that is specifically about this
assignment. Make sure to check the assignment prompt for specific
numbers used for national statistics and/or square footage. The video
may use different national statistics or solve for different square