MAT 240 SNHU Relationship BW Selling Price of Properties and Their Sizes Analysis

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 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.

Note: In the report you prepare for the sales team,
the response variable (y) should be the median listing price 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 median listing price and median square foot, report the mean, median, and standard deviation.
• 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:
    • 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?