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Final Project
Due: Friday, June 13, by midnight
(Submission method: see details at the end)
This is an individual project. Each student should work independently. On the cover
page of your report, please write your name (in chinese) and student number. The report
can be written in either English or Chinese.
Each student needs to find a dataset by yourself (e.g., from internet or books. You can
also use the datasets posted on class website, but it is better to use your own dataset if possi ble). Once you find a dataset, you need to analyze the dataset using the models and methods
learned in this course, state your results and conclusions, and write a report. Please see a
sample report posted on course website.
You must use at least one model learned in the course (e.g., multiple linear regression
models) in your report. Note that it is more desirable to use at least two models learned in
the course to analyze the same dataset, since each model has some limitations so one model
may not be enough. For example, you may consider a multiple linear regression model for a
continuous response (e.g., the response y may be happiness scores from 0 to 10), and you may
also consider a logistic regression model by converting the response into a binary variable (e.g.,
response y = 1 if happiness score is greater than 8 and y = 0 otherwise). As another example,
you may consider a linear mixed effects model for happiness scores measured repeatedly over
time, and you may also consider separate linear regression models for data at a few selected
time points. Then, you should compare the results from different models and provide some
comments (why they are similar or differnt). Different models for the same dataset allow
you to gain insights and overcome some limitations of each model. This appoach shows the
depth/quality of your data analysis.
Your report should contain the following materials:
• Abstract. Briefly summarize your study and results.
• Background and objectives of the study.
• Data description and summaries.
• Exploratory data analysis (EDA). This section may include summary statistics, graph ical displays of the data, and preliminary conclusions. No models are needed in this
section.
• Model building. This section should include statistical models, variable/model selection,
model parameter estimates (and their standard errors, p-values), model diagnostics, and
interpretation of results.
• Final conclusions and discussions. The discussion may include any limitations of your
results (e.g., if your results may be extended to larger population).
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• References. Here you can list any papers or books cited in your report.
• Appendix. Put any additional results, figures or tables, and R code here.
Requirements:
• Your report should have 5 – 10 pages, including tables and figures, but excluding cover
page and Appendix. You should put the most important materials in the main text and
other less important materials in the Appendix. Please see the sample report posted on
course website.
• You should summarize and interpret your computer outputs. Do not put raw R outputs
and notation in the main text of your report. Please put raw R outputs and code in the
Appendix. Assume the readers may or may not be familiar with software R.
Please start working on the project early, since it may take much more time than you expect!
Submission method:
• Late submissions will not be accepted.
• You must also submit your report to your usual course platform for Professor Shen Bao Hua. Your school must have the records of your reports. Our student representative Li
Tingting will collect the reports and send to Professor Shen.
• Your file name should be in the format of LastName-FirstName.pdf, e.g., Wu Lang.pdf.
Online office hours:
The instructor will provide remote online office hours via Zoom at times close to the
deadline. You will get the Zoom information in advance. You can ask any questions in the
Zoom office hours.
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