Capstone Project Description

Theme: Multiple Regression Analysis

Project Objective: The objective of this business statistics capstone project is to provide a hands-on, real world opportunity to integrate some of the statistical tools and concepts that you have been learning in class.

Project Description: You are to identify an issue that can be addressed by using a multiple regression analysis. You will collect raw data using an appropriate data collection method. This may include an observational study, a survey, experimentation, or a reputable secondary source (see chapter 1 in your book for more details).

Guidelines for Selecting a Project: Please be creative when choosing a project topic. Do not simply copy a regression topic from class or the book. Ideally, you should choose something that is applicable to your work or area of study in the Business School, your professional work experience, or something that genuinely interests you.

Required Model Specifications:

- The dependent variable should be a quantitative variable.

- While there is not a required minimum number of independent variables, you should be sure to address the central issue of the project.

You must have both quantitative and qualitative variables in your model.

- The sample size should be equal to at least 5 times the number of independent variables. All methods and sources for data collection should be fully documented and described in your report. Please be sure to discuss any possible biases or problems with your collection method.

Written Report: Your report should be professional, easy to read, and clean. Do not add extra "fluff" to make it longer. Keep it succinct, but comprehensive. Please include:

- A cover page with your project title and the names of every person in your group, plus relevant class information

- An executive summary which briefly describes your project goals, findings, conclusions and recommendations.

- An analysis section with your Excel output and a description in paragraph format of what you are analyzing and what it all means. After describing the analysis process, demonstrate the use of your model. More specifically, make a prediction using your regression equation. Discuss whether you think your prediction is accurate.

- A conclusion where you discuss your recommendations, any possible biases, what you might have done differently, and what your group's next steps would be if you were to use your information in a practical setting.

- An appendix that includes all your data and any analyses you did (in Excel or by hand).
Please remember to document anything you do or find! For example, if you create scatter plots or a correlation matrix, if you analyze the residuals, if you drop variables, if you encounter multicollinearity, if you use a stepwise regression, etc. I want to read about the process as well as the interpretation.

Research Proposal

The research will evolve around finding the factors which directly or indirectly affect the GDP of a country. The factors considered will be macroeconomic in nature & the country considered will be United States(Frost, 2013).

Variables

Dependent Variable: GDP of US in USD Trillion

Independent Variables: The Independent variables will be several Macroeconomic factors out of which the ones which are affecting the GDP effectively will be kept & rest will be removed from the final equation.
- Exports of Goods & Services ( Current US$ Trillion)
- Gross savings (current US$ Trillion)
- General government final consumption expenditure (current US$ Trillion)
- FDI (Current US$ Trillion)

Data Collection Method
- At First I will review the different journal articles & Literature in order to find out the perception of the researchers with relation to the macroeconomic variables affecting GDP.
- Then the next step will be to develop the hypothesis connecting the different parameters with that of the GDP of US.
- Second I will collect the data for those macroeconomic variables from the World Bank Data Source.
- Next Multiple Linear Regression will be performed by using SPSS(Garth, 2008).
- Finally the US GDP will be predicted for the coming year.