Let’s suppose you know the owner of a local real estate company in a mid-sized Northeastern city of the United States.  The name of your acquaintance’s company is, “North Valley Real Estate.”  Your acquaintance also has a large clientele and a top-notch staff of salespeople.  Luckily, the company also has some good data on the homes that have been sold by “North Valley Real Estate (NVRE)” throughout the community, over the years.  This data can be found as an Excel Dataset File located in the "Files" Section of the course (Left-hand Side Menu Bar).  As you may have guessed, the owner of NVRE wants to use the data to find insights into the home-selling market, and how this same insight can be used to increase their profit and business activity. 

As such, your knowledge of statistics and research applications can provide a valuable service to your friend and the NVRE company; and they have hired you as their consultant.   Using the data identified above, embark upon an analysis effort and creating an accompanying Report for the Owner of North Valley Real Estate.

Your purpose in the project is to glean information, knowledge, and insight from the provided data that can be used to increase the profitability of the Firm (NVRE).  Your research should align to the CLOs provided for the course and lean heavily on Units 4-8 of the course for the statistical application/analysis.  Specifically, the statistical tool of regression.

The paper is to be written in APA style, 7th edition.  Deviation from APA style is strongly discouraged, and will be penalized on the final draft.

Click here for Unit Learning Outcomes

    • Carry out a substantial research-based project
    • Demonstrate capacity to improve a business situation with data-driven analytics
    • Demonstrate capacity to recommend change through statistical analysis
    • Demonstrate an understanding of statistical applications and reporting frameworks
    • Analyze data and synthesize research findings


The time has come for you to bring everything together in your final report. Please ensure your final report contains all of the below information, as well as the contents provided in your previous assignments.  Further, ensure that you have incorporated all of the recommendations and guidance provided for the Rough Draft from your instructor.  Once completed, proofread, proofread, and then proofread again.  Make this paper a crisp and sharp example of statistical analysis and drawn conclusions, as well as professional and readable content/context.

Using the "North Valley Real Estate" Excel Dataset located in the "Files" Section of the course (Left-hand Side Menu Bar).  The Final Research Project for the course, and all supporting assignments to the project, will be executed using the "North Valley Real Estate" dataset.

Please make sure that your paper conforms to APA style requirements, 7th edition.

General Guidelines for a Successful Capstone Term Project Report include the following:

  1. Provide a general introduction, background, and purpose of the paper, with your thesis resting on the idea of using statistical analysis to achieve better business decision and increase profitability and business activities. Also, include a discussion of the real estate industry and the impacts that influence the health, viability, and success of the real estate marketplace; particularly in the Northeastern region of the U.S.
  2. State why the dependent variable has been chosen for analysis. Then make a general statement about the model you will be employing, for example:

“The dependent variable _______ is determined by variables ________, ________, ________, and __________.”

  1. Identify the primary independent variable and defend why it is important by stating:

“The most important independent variable in this analysis is ________ because _________.”

In your paragraphs, cite and discuss the research sources/references that support the thesis, i.e., the model you have chosen.

  1. Write the general form of the regression model (less intercept and coefficients), with the variables named appropriately so the reader can identify each variable at a glance:

Dep_Var = Ind_Var_1 + Ind_Var_2 + Ind_Var_3

    • For instance, a typical model would be written:

Price_of_Home = Square_Footage + Number_Bedrooms + Lot_Size.

      • Price_of_Home:  brief definition of dependent variable
      • Square_Footage:  brief definition of first/primary independent variable
      • Number_Bedrooms:  brief definition of second independent variable
      • Lot_Size:  brief definition of third independent variable
  1. Define and defend all variables, including the dependent variable, in a single paragraph for each variable. Also, state the expectations for each independent variable. These paragraphs should be in numerical order, i.e., dependent variable, X1, then X2, etc. In each paragraph, the following should be addressed:
      • How is the variable defined in the data source?
      • Which unit of measurement is used?
      • For the independent variables: why do the independent variables determine the dependent variable?
      • What sign is expected for the independent variable's coefficient, positive or negative? Why?
  1. Data Description: Describe the data and identify the data sources.  From which general sources and from which specific tables are the data taken?    Which year or years were the data collected.  Are there any data limitations?
  2. Presentation and Interpretation of Results. Write the regression (prediction) equation:

Dep_Var = Intercept + c1 * Ind_Var_1 + c2 * Ind_Var_2 + c3 * Ind_Var_3

  1. Identify and interpret the adjusted R2 (one paragraph). Define “adjusted R2,” what does the value of the adjusted R2 reveal about the model?  If the adjusted R2 is low, how has the choice of independent variables created this result?
  2. Identify and interpret the F-test (one paragraph). Using the p-value approach, is the null hypothesis for the F-test rejected or not rejected?  Why or why not?  Interpret the implications of these findings for the model.
  3. Identify and interpret the t-tests for each of the coefficients (one separate paragraph for each variable, in numerical order): Are the signs of the coefficients as expected?  If not, why not?  For each of the coefficients, interpret the numerical value.  Using the p-value approach, is the null hypothesis for the t-test rejected or not rejected for each coefficient?  Why or why not?  Interpret the implications of these findings for the variable.  Identify the variable with the greatest significance.
  4. Analyze multicollinearity of the independent variables (one paragraph); Generate the correlation matrix.  Define multicollinearity.  Are any of the independent variables highly correlated with each other?  If so, identify the variables and explain why they are correlated.  State the implications of multicollinearity (if found) for the model you have created for this analysis.
  5. Other (not required): If any additional techniques for improving results are employed, discuss these at the end of the paper. As for grading, the inclusion of additional statistical methods will be rewarded appropriately.
  6. Reference Page: Use the proper format to list the works cited and place the entire page in APA format 7th edition. Include at least 10 references from across the spectrum of possible reference sources (books, magazines, journals, periodicals, newspapers, videos, etc.)

General Paper Format:

Using headers and sub-headers to organize your paper accordingly.

      • Cover Page
      • Introduction
      • Background
      • Hypothesis
      • Variables
      • Data Set and Variable(s) description
      • Analysis
      • Conclusions
      • Recommendations
      • Reference page
      • Appendices (as needed)

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