24 Final project

The purpose of this project is for you to gain applied experience using linear models (i.e., regression and ANOVA). The final project will include: 1) a written report; 2) a tutorial with reproducible results; 3) a peer review; and 4) a professional presentation.

You may work alone or with another person. If you choose to work with another person, include a few short paragraph that details your contribution to the work and person’s contributions to the work.

  1. Decide on a topic to research. The topic could be a questions of scientific or social interest or related to the theory of linear models. For applied questions, you will have to find a relevant data set. For theoretical questions you might use data, conduct a simulation study, or show/prove mathematical results.

  2. Submit a proposal by 11:59 pm Friday, June 21. The proposal should answer the questions below in one page or less.

    • What topic do you plan to research?
    • What data are available or what methods do you plan to use for a more theoretical question?
    • Why does your topic warrant further research?
    • Find an appropriate scientific journal that you would consider submitting your report to. If you are not interested in a scientific audience, choose another reasonable audience. For example, many sports teams hire data scientists to analyze sports data. For this example, you could analyze sports data and write a report to the managers and coach of the sports team. Please explain what journal (or audience) you choose and why do you think your report is appropriate for that journal (or audience). The journal you choose will be your target audience for the written report.
  3. Prepare a written report. Format your paper so that it is appropriate for the journal or audience that you proposed in your proposal. I have provided some guidelines below, but the exact format and length of your report will be specific to the journal you chose.

    • Abstract (100-300 word summary of your report)
    • Introduction (500-1000 words)
    • Methods (500-1000 words)
    • Results (500-1000 words)
    • Discussion (500-1000 words; The discussion should place your results into the context of existing work and explore what additional research still needs to be done.).
    • Literature cited (Please use a professional citation format).
  4. Prepare a tutorial that includes all relevant statistical code and important results in a manner that is fully reproducible.

  5. Prepare a professional presentation.

    • Presentations should be about 10 min in length
    • More information will be given later in the semester about this
  6. Arrange for your written report and tutorial to be peer reviewed by another person/group. Reviewer(s) will submit a review to the author(s) and also upload it to Canvas by 11:59 pm on Wednesday July 24.

  7. On or before 11:59 pm on Sunday July 28 please

    • A single file that contains your written report (#3)
    • A single file that contains your tutorial (#4)
    • A single file that contains your presentation (#5)
    • If you choose to work with a partner, include a short paragraph that details each persons contributions to the final project

24.1 Grading Rubric

Category Points
Proposal (Due June 21) 10
Peer review (Due July 24) 10
Report (Due July 28) 40
Tutorial/Reproducible analysis (Due July 28) 20
Presentation (More info to come; Due prior to July 26) 20

24.2 Examples of A and A+ quality work from a similar class

  • This is an example of a classic project link
  • This is an example of a more creative project (note, tutorial is not given in the link) link
  • This is an example of a class project by a PhD student that was turned in to a scientific publication link
  • This is an example of a class project by an undergraduate student that was turned in to a scientific publication link
Efron, Bradley, and Robert J Tibshirani. 1994. An Introduction to the Bootstrap. CRC press.
Faraway, J. J. 2014. Linear Models with r. CRC Press.
Hesterberg, Tim C. 2015. “What Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statistics Curriculum.” The American Statistician 69 (4): 371–86.
Powell, Larkin A. 2007. “Approximating Variance of Demographic Parameters Using the Delta Method: A Reference for Avian Biologists.” The Condor 109 (4): 949–54.
Ver Hoef, Jay M. 2012. “Who Invented the Delta Method?” The American Statistician 66 (2): 124–27.