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Final Project Guideline 1


Final Project Guideline
1. Objective of the project:
Using the linear regression analysis, we will analyze the recent Covid-19 dataset which has been
released through The Community Profile Report, provided by White House COVID-19 Task Force. To
understand the detail about the data, see the website:
2. Learning outcome:
• You are able to import the dataset on R and understand the structure.
• You are able to analyze the dataset to find some possible association among the variables.
• You are able to construct the linear model to predict one variable from another variable.
• You are able to use the linear model to predict for a future observation.
• You are able to discuss the implication or limitation of the model in the context of the data.
3. Project outline
• You will be expected to work individually or in groups of maximum 7 students to complete the
• If you choose to work with a group, please
• Project report: You need to submit two files: 1) final report (in pdf) and 2) R program file. The due
date for the report is June 5th, 11pm on CCLE.
4. Grading guideline
• The group project takes 15% of your final grade.
• You will have 2 checkpoint assignments throughout the quarter. If you miss a deadline, you will
receive 0 points.
o 3% - 2 Checkpoint assignments (graded based on completion)
o 12% - Written Report and R file submission
5. Final report formation
• Length: Maximum 6 pages including plots and tables. Single space with 11pt. You may put
additional plots in Appendix.
• Structure:
o Introduction
§ Provide the basic information of the dataset including its source.
§ State your interest and motivation for the research question.
• Why do you include the specific variables in your analysis?
• What does it mean to answer the research question in our community or
• Why is the linear regression model appropriate to answer the research
o Data description
§ Describe EACH of the two numerical variables of your choice.
• Report their summary statistics, such as mean, standard deviation, and etc.
• Visualize each of the two variables in the most efficient graphing method.
• Describe the distribution of the variables based on the graphs.
§ Describe the association of the two variables using scatter plot and correlation
coefficient. Interpret your observations.
o Results and interpretation
§ Run the linear regression analysis on the two variables.
§ Report the linear model and interpret the results, including slope and intercept, in
the context of the study.
§ Assess the model using a residual plot and R-square.
o Discussion
§ Summarize your project and result.
§ Discuss if your result makes sense in the real world situation. If you can find any
literature or articles that can support your result, then that can be a plus.
§ Discuss the limitation (if any) of the project.
6. Suggested timeline
• Week 1-4: Find group members if you want to work in a group. Post the list of group members at
• Week 5-6: Understand the project and understand the data set through the source website.
Explore variables and observations, and look for your own research question. (*You learn all R
commands you need to run the regression analysis from Lab 3 during week 5 and 6.)
o 1st check up assignment will ask your understanding about the dataset. (due on May 12th)
• Week 7-8: Import the data set on R, run the analysis, and get the result.
o 2nd check up assignment will ask you to write the brief result of the analysis. (due on May 16th)
• Week 9-10: Interpret the result in the context of the data. Write-up the report.
o Final report submission due on June 5th.

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