Unit 2: introduction/background of project topic
Unit 2: introduction/background of project topic
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:
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.
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 __________.”
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.
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
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?
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?
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
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?
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.
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.
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.
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.
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.
Data Set and Variable(s) description
Appendices (as needed)
All due dates are Sunday, 11:59 p.m. CT for the units delineated below.
Unit 2: Introduction/Background of Project Topic
Unit 3: Variable Identification and Hypothesis
Unit 4: Identification and Listing of References (10)
Unit 5: Term Project Outline
Unit 6: Term Project Rough Draft
Unit 8: Submit Final Term Project