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 us

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 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 PDF 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. Include a trend line and the regression equation. Label the axes.
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 1,800 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 footage values.

MAT-240 Module 2 Assignment

You can also use the following tutorials for support as you develop the report:

What to Submit

Submit your completed Module Two Assignment Template as a Word document that includes your response, supporting charts, and Excel file.

Selling Price and Area Analysis for D.M. Pan National Real Estate Company 2

[Note: To complete this template, replace the bracketed text with your own content. Remove this note before you submit your outline.]

Report: Selling Price and Area Analysis for D.M. Pan National Real Estate Company

[Your Name]

Department of Mathematics, SNHU

MAT 240: Applied Statistics

[Instructor Name]

[Due Date]

Report: Selling Price and Area Analysis for D.M. Pan National Real Estate Company

Introduction

[Include in this section a brief overview, including the purpose of the report.]

Generate a Representative Sample of the Data

[Present your simple random sample of 30, including the region you selected for your sample. Then identify 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 reflective of the national market. Compare and contrast your regional sample with the national population using the National Statistics and Graphs document found in the Module Two Assignment Guidelines and Rubric.

Explain how you have made sure that the sample is random. Explain your methods to get a truly random sample.]

Generate Scatterplot

[Insert a scatterplot graph of the sample using the x and y variables. Include a trend line and the regression equation. Label the axes.]

Observe Patterns

[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 scatterplot.]

[What do you see as the shape (linear or nonlinear)?]

[If you had an 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?]

Summary Statistics for MAT 240 Real Estate Data (for dataset in Modules 2, 3, and 4)

n Mean Median Std. Dev. Min Q1 Q3 Max Listing price ($)

1,000 342,365 318,000 125,914 135,300 265,250 381,600 987,600

Cost per square foot ($)

1,000 169 166 41 71 139 191 344

Square feet

1,000 2,111 1,881 921 1,101 1,626 2,215 6,516

This graph shows the frequency for listing price.

This graph shows the frequency for square feet.

National Summary Statistics and Graphs Real Estate Data