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.
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 County Data document 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.
and the predictor variable (x) should be the median 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 median listing price and the median square foot
Analyze Your Sample
Discuss how the regional sample created is 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.
Create a scatterplot of the x and y variables noted above and include a trend line.
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 1,200 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 tutorials for support as you develop the report:
Downloading Office 365 Programs
Random Sampling in Excel
Scatterplots in Excel
Descriptive Statistics in Excel
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