Pick a dataset, any dataset…
…and do something with it. That is your final project in a nutshell. More details below.
The final project for this class will consist of a statistical analysis on a dataset of your own choosing. The dataset may already exist, or you may collect your own data using a survey or by conducting an experiment. If possible, choose a dataset which you are interested in and may be useful for another class project or your research if you are working in a lab. The goal of this project is for you to demonstrate proficiency in the techniques we have covered in this class and apply them to a novel dataset in a meaningful way.
In order for you to have the greatest chance of success with this project it is important that you choose a manageable dataset. This means that the data should be readily accessible and large enough that multiple relationships can be explored. As such, your dataset must have at least 50 observations and between 10 to 20 variables (exceptions can be made but you must speak with me first). The dataset’s variables should include categorical variables, discrete numerical variables, and continuous numerical variables.
All analyses must be done in RStudio. If you are using a dataset that comes in a format that we haven’t encountered in class, make sure that you are able to load it into RStudio as this can be tricky depending on the source. If you are having trouble ask for help before it is too late.
Reusing datasets from class: Do not reuse datasets used in examples / homework in the class.
This is basically your HW 04, so see instructions there.
After providing the description of your dataset and research question in the introduction you must apply what you have learned about descriptive statistics, visualizations, correlation and regression, and inference (hypothesis testing and confidence intervals) to your dataset. The goal is not to do an exhaustive data analysis i.e., do not calculate every statistic and procedure you have learned for every variable, but rather let me know that you are proficient at using R, and that you are proficient at interpreting and presenting the results. Focus on methods that help you begin to answer your research questions. You do not have to apply every statistical procedure we learned, but you must apply some. Also pay attention to your presentation. Neatness, coherency, and clarity will count.
10 minutes maximum, and each team member should say something substantial.
You can use any software you like for your final presentation, including R Markdown to create your slides. There isn’t a limit to how many slides you can use, just a time limit (10 minutes total). Each team member should get a chance to speak during the presentation. Your presentation should not just be an account of everything you tried (“then we did this, then we did this, etc.”), instead it should convey what choices you made, and why, and what you found.
Your submission should include
Style and format does count for this assignment, so please take the time to make sure everything looks good and your data and code are properly formated.
You’re working in the same repo as your teammates now, so merge conflics will happen, issues will arise, and that’s fine! Commit and push often, and ask questions when stuck.
Review the grading guidelines below and ask questions if any of the expectations are unclear.
Make sure each team member is contributing, both in terms of quality and quantity of contribution (we will be reviewing commits from different team members).
Set aside time to work together and apart (physically).
When you’re done, review the .md document on GitHub to make sure you’re happy with the final state of your work. Then go get some rest!
The project is very open ended. You should create some kind of compelling visualization(s) of this data in R. There is no limit on what tools or packages you may use, but sticking to packages we learned in class (tidyverse). You do not need to visualize all of the data at once. A single high quality visualization will receive a much higher grade than a large number of poor quality visualizations.
Your write up must also include a one to two page conclusion and discussion. This will require a summary of what you have learned about your research question along with statistical arguments supporting your conclusions. Also critique your own methods and provide suggestions for improving your analysis. Issues pertaining to the reliability and validity of your data, and appropriateness of the statistical analysis should be discussed here. A paragraph on what you would do differently if you were able to start over with the project or what you would do next if you were going to continue work on the project should also be included.
In your write up your code should be hidden (echo = FALSE) so that your document is neat and easy to read. However your document should include all your code such that if I re-knit your Rmd file I should be able to obtain the results you presented.
Exception: If you want to highlight something specific about a piece of code, you’re welcomed to show that portion.
You are to complete the assignment as a team. All team members are expected to contribute equally to the completion of this assignment and group assessments will be given at its completion - anyone judged to not have sufficient contributed to the final product will have their grade penalized. While different teams members may have different backgrounds and abilities, it is the responsibility of every team member to understand how and why all code and approaches in the assignment works.
| Total | 100 pts |
|---|---|
| Presentation | 35 pts |
| Write up | 40 pts |
| Classmates’ scores | 5 pts |
| Team peer evaluaiton | 10 pts |
| Repo and document organization | 10 pts |
Team peer evaluation: You will be asked to fill out a survey where you rate the contribution of each team member out of 10 points. Filling our the survey is a prerequisite for getting credit on the team member evaluation. If you are suggesting that an individual did less than 20% of the work, please provide some explanation. If any individual gets an average peer score indicating that they did less than 10% of the work, this person will receive half the grade of the rest of the group.
Grading of the project will take into account the following:
A general breakdown of scoring is as follows:
Late penalty: