QUANTITATIVE ANALYSIS PROJECT /CASE STUDY

Spring 2018

Each student is expected to work independently to complete this assignment. The aim of the project is to help students apply quantitative analysis techniques including statistical methods particularly Regression, forecasting and Correlation Analysis to general empirical studies relating to real business situations. The project requires students to analyze problems, recommend a solution, and compose a written report.

After completing this project, students should be able to

  • Collect relevant data.
  • Manipulate the data using various techniques studied in class.
  • Analyze the data to draw informed conclusions.

SUGGESTED PROJECT AREAS

  • Using data from large retail stores such as Walmart, Macy, Home Depot, Lowes, Costco or any large retail shop of your choice, find the relationship between sales and advertising. Or find the relationship between any relevant variables identifying the dependent and the independent variable (s).
  • Find the relationship between homes prices in the State of Maryland and home sales. You may collect data from a county, a city or the whole state. You may also find data on the number of rooms per home, number of baths and the year of construction or any relevant factor that may affect home sale no mentioned in the list above.

METHODOLOGY

Collect data for the last 20 years for yearly data, two years for monthly data, one year for weekly data or five years for quarterly data. State the source of data and provide a web link for the data collected. Once the data is collected and after approval, you will write an introduction which include the issue you are trying to analyze, the method that you will use to study the problem and the expected outcome. You will use techniques such as regression analysis, forecasting and correlation analysis to measure the relationships between the variables, and perform possible predictions on the variables studied. Once you obtain and present the results, you will need to analyze these results to draw a conclusion by commenting on the correlations and forecasted values. This should be followed by a conclusion.

EVALUATIONS

  • The key success factor for this project is to demonstrate a systematic approach to choose the type of data and the tools of analysis. This should be done on Excel. You should explain the rationale for adopting a particular method of analysis.
  • A typed, double-space paper that contain an introduction, a section describing your methodology, your calculations and a conclusion section that summarizes the results of your calculations. The formulas used should be shown in detail, and the calculations shown clearly. All cited work and source of information must be listed in the reference list.
  • You should each keep a log on what you have been assigned to do and what you have accomplished.
  • The project will be evaluated by me and you will receive a discounted grade if the there is significant discrepancy.

PROJECT TIMELINE

  1. By 9/30, you should have the historical time series for your project arranged in a spreadsheet.
  2. By 10/15, you should have done the regression and forecasts.
  3. By 10/25, you should have evaluated the relationship between the variables studied done the necessary tests and a discussion of the results.
  4. By 11/05, you should have written the whole project.
  5. Final touches are added during the last week before due date (11/10).

Each project which will not be less than 5 pages will be written using the following guidelines and contents:

  • Title page (Include project title and your name) (5%)
  • Introduction: Problem being studied, purpose and justification of the study (20%)
  • Data analysis – various calculations (45%)
  • Interpretation of results (10%)
  • Findings and conclusion. (10%)
  • Appendices: Tables and Figures. (5%
  • References (5%)