Reference no: EM132227638
PROJECT DESCRIPTION
If possible, your project should draw from your work environment or career interest. But this is NOT a requirement. Begin by identifying a response variable (i.e. dependent variable, Y). The dependent variable is the problem you wish to examine. Afterward, think of two or more independent variables you believe may help explain the variations in your response variable. Due to additional regression issues associated with times series data, I strongly recommend that you use cross-sectional data in your regression. Examples of areas of study that may offer researchable ideas are labor problems, corporate performance, market analysis, accounting and internal control, supply chain, consumer behavior, social/political issues, international trade, education reform - to name a few.
Section 1: Introduction
Provide a brief background of the study. What is the study about? What is the significance of the study? Are there possible benefits that may come out of the findings? If there are any background studies, cite them here.
Section 2: Data & Methodology
Describe your DATA and regression EQUATION. Show the statement of hypothesis and define the variables. What manner of relationship do you expect to find?
Section 3: Analysis and Results
Present a summary of your final results. Be sure to show a summary of the results of any transformations you have performed. Also, provide a brief explanation of the results. Are the results consistent with your expectations prior to the study? (Max 1 page)
Section 4: Conclusions and Study Limitations
Briefly summarize your conclusions. Any limitations and improvements?
NOTE
• Your report should be prepared in Word. You may initially prepare your results tables on spreadsheet and then copy them over to your main Word file.
• Do NOT include your dataset in your report. You may place it in the appendix.
• Your score will depend on
1. The correctness of your model specification
2. Interpretation of the regression model, analysis, and results
3. Clarity of your overall research writing.
• To maximize your score, you should include (a) a test for multicollinearity, if using 3 or more independent variables, as demonstrated in the video guide; (2) test for heteroscedasticity - if your dataset is cross-sectional - by presenting a residual plot (Goldfeld-Quandt test not necessary), and if you believe you might have unequal error variance, implement at least ln(Y) in a transformation; (3) perform a Durbin- Watson (DW) test for residual correlation, if using time series data, and simply show your DW results - No need to perform any correction. In this way, you would have fully demonstrated not only your understanding of Multiple Regression but also, how to check for, and possibly correct, any violations of the assumptions of the Ordinary Least Squares (OLS) Method. Also, feel free to utilize any nonlinear transformations you believe would improve the fit of your data.
Attachment:- Term Project - Description.rar