Midterm project outline. Find a time series dataset of your choice. Carry out a time series analysis, taking advantage of what we have learned so far in this course. Write a report, submitted on Canvas as a zip file by the deadline, 11:59pm on Monday 2/21. The zip file should contain the following:

  1. A file called unblinded.Rmd and its compiled version unblinded.html which are the full report file. This version should have the names of the group and should include a section explaining the contributions of group members. These are the files that the grader is expected to spend most time with, though other files may also be inspected.

  2. A file called blinded.Rmd and its compiled version blinded.html in which all identifying text is removed. This version will be used for anonymous peer review and posted on the course website.

  3. Data files and any other files needed to compile the Rmd on a standard R or Rstudio environment. You can assume that the grader and peer reviewers will install required libraries as needed.


Groups. There will be randomly assigned groups of approximately 3 people. As pointed out in the syllabus, in special situations you can request to write an individual midterm project. This may be appropriate if you have a particular dataset or scientific question that has motivated your interest in learning time series analysis. You must request this before groups are assigned. Once you are in a group you have to come to an agreement with your group on what data to analyze. You will not be able to request your own group partners - in order to treat everyone fairly, groups will be randomized.


Choice of data. The time series should hopefully have at least 100 time points. You can have less, if your interests demand it. Shorter data needs additional care, since model diagnostics and asymptotic approximations become more delicate on small datasets. If your data are longer than, say, 1000 time points, you can subsample if you start having problems working with too much data. Come ask the instructor or GSI if you have questions or concerns about your choice of data.


Data privacy and project anonymity. The projects, together with their data and source code, will be posted anonymously on the class website unless you have particular reasons why this should not be done. For example, you may have access to data with privacy concerns. The projects will be posted anonymously. After the semester is finished, you can request for your name to be added to your project if you want to.


Expectations for the report. The report will be graded on the following categories.


Plagiarism. If material is taken directly from another source, that source must be cited and the copied material clearly attributed to the source, for example by the use of quotation marks. Failing to do this is plagiarism and will, at a minimum, result in zero credit for the scholarship category and the section of the report in which the plagiarism occurs. Further discussion of plagiarism can be found in On Being a Scientist: A Guide to Responsible Conduct in Research: Third edition (2009), by The National Academies Press. Here is how the Rackham Academic and Professional Integrity Policy describes plagiarism:

11.2.2 Plagiarism

Includes:

Representing the words, ideas, or work of others as one’s own in writing or presentations, and failing to give full and proper credit to the original source.

Failing to properly acknowledge and cite language from another source, including paraphrased text.

Failing to properly cite any ideas, images, technical work, creative content, or other material taken from published or unpublished sources in any medium, including online material or oral presentations, and including the author’s own previous work.