1. Part I: Identify a multivariate dataset of interest. Do a descriptive analy- sis, and visualize the data to convey the characteristics of the variables in your dataset.
2. Part II: Formulate a research question tailored to your dataset. This should be the thesis of your final report, and should describe exactly what you are interested in studying/answering. This part also should include the description of the mathematical model you intend to use to answer your research question. This could be any method/approaches or combi- nation of multiple methods/approaches that you learned in this course.
3. Part III: Implement a strategy or solution to your research question posed in Part II and report your results/findings. This should be the main section of your final report.
4. Part IV: You need to provide the codes you implemented on your data to get the results. I am open to any software (e.g. R, Python, etc.) but you should provide your complete code so that I can run that and check your results.
Grading Scheme: The most important part of the project will, of course, be your final report, so make sure it is complete and understandable. Make sure to complete all three parts listed above and try to come up with some interesting conclusions after finishing part III. Don’t just list a bunch of results but instead try to provide a clear answer to your question posed in part II. You will also be graded on the clarity of your presentation. Remember the project is worth 30% of your grade so please do not leave it to the last minute. You need to provide proper citation.
Here is an idea about the grading scheme covering the tasks:
1. Project proposal: 20%
2. Final Report: 60%3. Presentation: 20%
Timeline : You are expected to follow this time plan to successfully complete your project work.
1. Project proposal due on 21 October (Friday Midnight), 2022
2. You will have feedback (approved or need modification) on your proposed project by the following weekend.
3. Updated (if needed) project proposal is due within 3 days after you re- ceived the feedback
4. Progress (mainly, the literature review, and part I) of your work will be followed in the fist week of November
5. First draft (optional but will have some extra credit point) of your work is due on third week of November, and you may have feedback on your submission to improve your work.
6. FINAL SUBMISSION is due on 04 December (Midnight), 2022.
7. Presentation
• December 5 - December 16.
• 5-7 Minutes to present your work • 2 Minutes for Q/A
1. Have you included an introduction/background of your project?
2. What is/are the research question(s) of your project?
3. What method(s) are you planning to use to meet those question(s)? 4. What are the data and data source(s)?
5. Write about a tentative plan for accomplishing the project.
Areas of Work: The following topics should give you an idea about the topic you can work on your project. You are welcome to bring your own ideas/topic to the project.
1. Multivariate Normal Distribution (MVN) 2. Multivariate Linear Regression
3. Principal Component Analysis (PCA)
4. Factor Analysis (FA)
5. Cluster Analysis (CA)
6. Discriminant Analysis (DA)
Data and Data Sources: I will provide your some datasets, and sources to get data. You are welcome to use those dataset. However, if you want to work on any other dataset of your interest with proper source and justification, you are more than welcome to do so. You are also welcome to bring your research work (if any) which is not submitted yet for any other courses/purposes.
