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

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

Question Description

I’m working on a statistics writing question and need an explanation to help me understand better.

Scenario

Smart businesses in all industries use data to provide an intuitiveanalysis of how they can get a competitive advantage. The real estateindustry heavily uses linear regression to estimate home prices, as costof housing is currently the largest expense for most families.Additionally, in order to help new homeowners and home sellers withimportant decisions, real estate professionals need to go beyond showingproperty inventory. They need to be well versed in the relationshipbetween price, square footage, build year, location, and so many otherfactors that can help predict the business environment and provide thebest advice to their clients.

Prompt

You have been recently hired as a junior analyst by D.M. Pan RealEstate Company. The sales team has tasked you with preparing a reportthat examines the relationship between the selling price of propertiesand their size in square feet. You have been provided with a Real Estate County Datadocument that includes properties sold nationwide in recent years. Theteam has asked you to select a region, complete an initial analysis, andprovide 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 thepredictor 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.
    • 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?

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