STA 210 - STA 210 Final Project

Timeline

Proposal due Friday, October 9

Draft report due Wednesday, October 28

Peer feedback due Thursday, November 5

Final report due Tuesday, November 17

Presentation video, slides, repo due Friday, November 20

Presentation comments due Sunday, November 22

Introduction

TL;DR: Pick a dataset and do a regression analysis. That is your final project.

The goal of the final project is for you to use regression analysis to analyze a dataset of your own choosing. The dataset may already exist or you may collect your own data by scraping the web.

Choose the data based on your group’s interests or work you all have done in other courses or research projects. The goal of this project is for you to demonstrate proficiency in the techniques we have covered in this class (and beyond, if you like!) and apply them to a dataset to analyze it in a meaningful way.

Logistics

You will work on the project with your lab groups.

The four primary deliverables for the final project are

A written, reproducible report detailing your analysis
A GitHub repository corresponding to your report
Slides + a video presentation
Formal peer review on another team’s project

Overall grading

The grade breakdown is as follows:

Total	115 pts
Project proposal	10 pts
Written report	50 pts
Slides	15 pts
Repository	5 pts
Video presentation + comments	20 pts
Peer review	15 pts

Note: No late projects are accepted.

Data sources

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. Therefore, your dataset must have at least 100 observations and at least 10 variables (exceptions can be made but you must speak with me first). The response variable may be quantitative or categorical, and the data set should both quantitative and categorical variables that can be potential predictors.

If you are using a dataset that comes in a format that we haven’t encountered in class (for instance, a .DAT file), 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: You may not reuse datasets used in examples / homework in the class.

Some resources that may be helpful:

Additional resources:

All analyses must be done in RStudio, and your final written report and analysis must be reproducible. This means that you must create an R Markdown document attached to a GitHub repository that will create your written report exactly upon knitting.

Project proposal

There are two main purposes of the project proposal:

To help you think about the project early, so you can get a head start on finding data, reading relevant literature, thinking about the questions you wish to answer, etc.
To ensure that the data you wish to analyze, methods you plan to use, and the scope of your analysis are feasible and will help you be successful for this project.

Include the following in the proposal:

Section 1 - Introduction

The introduction section includes

an introduction to the subject matter you’re investigating
the motivation for your research question (citing any relevant literature)
the general research question you wish to explore
your hypotheses regarding the research question of interest.

Section 2 - Data description

In this section, you will describe the data set you wish to explore. This includes

description of the observations in the data set,
description of the response variable,
general description of the variables that will be considered as predictors,
description of how the data was originally collected (not how you found the data but how the original curator of the data collected it).

Section 3 - Glimpse of data

Use the glimpse function print a summary of the data at the end of your proposal.

Place your data in the /data folder of the repo. Later on you will include the codebook in the README of the data folder.

Submission

Submit the PDF of your proposal in the Project Proposal assignment on Gradescope. Be sure to select the names of all team members in the submission. The proposal is due on Friday, October 9.

Proposal grading

Total	10 pts
Introduction	4 pts
Data description	4 pts
Glimpse of data	2 pts

Written report

Your written report must be done using R Markdown. All team members must contribute to the GitHub repository, with regular meaningful commits.

Before you finalize your write up, make sure the printing of code chunks is off with the option echo = FALSE.

Your final report must match your GitHub repository exactly. The mandatory components of the report are below. You are free to add additional sections as necessary. The report, including visualizations, should be no more than 15 pages long. There is no minimum page requirement; however, you must comprehensively address all of the aspects mentioned below.

Please be judicious in what you include in your final write-up.

The written report is worth 50 points, broken down as follows

Total	50 pts
Introduction/data	8 pts
Methodology	17 pts
Results	17 pts
Discussion	8 pts

Introduction and data

This section includes an introduction to the project motivation, data, and research question. Describe the data and definitions of key variables. It should also include some exploratory data analysis. All of the EDA won’t fit in the paper, so focus on the EDA for the response variable and a few other interesting variables and relationships.

Grading criteria

(8 pts) The research question and motivation are clearly stated in the introduction. The data are clearly described, including a description about how the data were originally collected and a concise definition of the relevant variables for the analysis. The explanatory data analysis helps the reader better understand the observations in the data set and interesting relationships between the variables.

Methodology

This section includes a brief description of your modeling process. Explain the reasoning for the type of model you’re fitting, predictor variables considered for the model including any interactions. Additionally, show how you arrived at the final model by describing the model selection process, any variable transformations (if needed), and any other relevant considerations that were part of the model fitting process.

Grading criteria

(17 pts) The analysis steps are appropriate for the data and research question. The group used a thoughtful approach to select the final model that took into account potential interaction effects and addressed violations in model conditions. The model conditions and diagnostics are thoroughly and accurately assessed as appropriate for the model. If violations of model conditions still exist, there was a reasonable attempt to address them, i.e. based on what we’ve learned this semester.

Results

This is also where you will output the final model and include a brief discussion of the model assumptions, diagnostics, and any relevant model fit statistics.

Additionally, interpret the key results from the model. The goal is not to interpret every single variable in the model but rather to show that you are proficient in answering your research question using the results from the analysis. Focus on the variables that help you answer the research question and that provide relevant context for the reader.

Grading criteria

(17 pts) The model fit is clearly assessed, and interesting findings from the model are clearly described. Interpretations of model coefficients are used to support the key findings and conclusions, rather than merely listing the interpretation of every model coefficient. If the primary modeling objective is prediction, the model’s predictive power is assessed.

Discussion

This section is a 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 also be discussed here. Include 1 - 2 paragraphs 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.

Grading criteria

(4 pts) Overall conclusions from analysis are clearly described. The group has thoughtfully considered potential limitations of their data and/or analysis and presented potential ideas to address these limitations and avenues to explore in future work.

(4 pts) The final write up is neatly organized with clear section headers and appropriately sized figures with informative labels. All code, warnings, and messages are suppressed. Overall, the document would be presentable in a business or research setting.

Peer review

Critically reviewing others’ work is a crucial part of the scientific process, and STA 199 is no exception. You will be assigned a team to review. As part of the review process, you must provide your partner team a copy of your current report by Wednesday, October 28. After giving the report to your partner team, they will have until Friday, November 5 to provide a detailed critique about the written report and data analysis. This feedback is intended to help you create a high quality final project, as well as give you experience reading and constructively critiquing the work of others.

During the peer review process, you will be provided read-only access to your partner team’s GitHub repo. Provide your review in the form of GitHub issues to your partner team’s GitHub repo. Peer review will be graded on the extent to which it comprehensively and constructively addresses the components of the partner team’s report: the research context and motivation, exploratory data analysis, and any inference, modeling, or conclusions.

As you work on the draft, the focus should be on the analysis and less on crafting the final report. Your draft must include a reasonable attempt at each analysis component - exploratory data analysis, modeling, and preliminary inferential conclusions.

The final report is due on Tuesday, November 17.

Presentation Slides

In addition to the write-up, your team must also create presentation slides that summarize and showcase your project. Introduce your research question and dataset, showcase visualizations, and provide some conclusions. These slides should serve as a brief visual accompaniment to your write-up and will be graded for content and quality. For submission, convert these slides to a .pdf document to be submitted to Gradescope. The slides are due on Friday, November 20.

The slide deck should have no more than 6 content slides + 1 title slide. Here is a suggested outline as you think through the slides; you do not have to use this exact format for the 6 slides.

Title Slide
Slide 1: Introduce the topic and motivation
Slide 2: Introduce the data
Slide 3: Highlights from EDA
Slide 4: Final model
Slide 5: Interesting findings from the model
Slide 6: Conclusions + future work

Video presentation

By Friday, November 20, your group will upload a video presentation of your project to Warpwire. Note that all members must present, and that a ten-minute time limit is strictly enforced.

For the presentation, you can speak over your slide deck, similar to the lecture content videos. I recommend using Zoom to record your presentation; however, you can use whatever platform works best for your group. Below are a few resources to help you record video presentations:

You will post the presentation video in Warpwire, which is accessible from the the course Sakai site (bottom of the left-hand tool bar).

To upload your video to Warpwire:

Click the Warpwire tab in the course Sakai site.
Click the “+” and select “Upload files”.
Locate the video on your computer and click to upload.
Once you’ve uploaded the video to Warpwire, click to share the video and make a copy of the video’s URL. You will need this when you post the video in the discussion forum.

To post the video to the discussion forum:

Click the Presentations tab in the course Sakai site.
Click the Presentations topic.
Click “Start New Presentation”.
Make the title “Your Team Name: Project Title”. For example, “Teaching Team: Analysis of Cars in the US”.
Click the Warpwire icon (between the flag and shopping cart icons).
Select your video, then click “Insert 1 item.” This will embed your video in the conversation.
Under the video, paste the URL to your video.
You’re done!

You can see the Teaching team example in Sakai.

Presentation comments

Each student will be assigned 2 presentations to watch. Click here to see your viewing assignments.

Watch the group’s video, then click “Reply” to post a question for the group. You may not post a question that’s already been asked on the discussion thread. Additionally, the question should be (i) substantive (i.e. it shouldn’t be “Why did you use a bar plot instead of a pie chart”?), (ii) demonstrate your understanding of the content from the course, and (iii) relevant to that group’s specific presentation, i.e demonstrating that you’ve watched the presentation.

You may start posting questions and comments on Sat, Nov 21 at 12 EST. All questions must be posted by Sun, Nov 22 at 11:59p EST.

This portion of the project will be assessed individually.

Project repository

In addition to your Gradescope submissions, we will be checking your GitHub repository. This repository should be contributed to equally by all team members and should include

RMarkdown file (formatted to clearly present all of your code and results) that will output the proposal and write-up in one document
Meaningful README file on the GitHub repository that contains a codebook for relevant variables
Dataset(s) (in csv or RData format, in a /data folder)
Presentation (if using Keynote/PowerPoint/Google Slides, export to PDF and put in repo, in a /presentation folder)

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 formatted.

The repository must be finalized by Friday, November 20.

Peer teamwork evaluation

You will be asked to fill out a survey where you rate the contribution and teamwork of each team member by assigning a contribution percentage for each team member. Filling out the survey is a prerequisite for getting credit on the team member evaluation. If you are suggesting that an individual did less than half the expected contribution given your team size (e.g., for a team of four students, if a student contributed less than 12.5% of the total effort), please provide some explanation. If any individual gets an average peer score indicating that this was the case, their grade will be assessed accordingly.

Additional notes and tips

The project is very open ended. For instance, in creating a compelling visualization(s) of your data in R, there is no limit on what tools or packages you may use. 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.

Before you finalize your write up, make sure the printing of code chunks is turned off with the option echo = FALSE. In addition to code chunks, ensure all messages are turned off with the options warning = FALSE and echo = FALSE.

Finally, pay attention to details in your write-up and presentation. Neatness, coherency, and clarity will count.

Tips

Ask questions if any of the expectations are unclear.
Code: 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 welcome to show that portion.
Merge conflicts will happen, issues will arise, and that’s fine! Commit and push often, and ask questions when stuck.
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).
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.

Grading summary

Grading of the project will take into account the following:

Content - What is the quality of research and/or policy question and relevancy of data to those questions?
Correctness - Are statistical procedures carried out and explained correctly?
Writing and Presentation - What is the quality of the statistical presentation, writing, and explanations?
Creativity and Critical Thought - Is the project carefully thought out? Are the limitations carefully considered? Does it appear that time and effort went into the planning and implementation of the project?

A general breakdown of scoring is as follows:

90%-100%: Outstanding effort. Student understands how to apply all statistical concepts, can put the results into a cogent argument, can identify weaknesses in the argument, and can clearly communicate the results to others.
80%-89%: Good effort. Student understands most of the concepts, puts together an adequate argument, identifies some weaknesses of their argument, and communicates most results clearly to others.
70%-79%: Passing effort. Student has misunderstanding of concepts in several areas, has some trouble putting results together in a cogent argument, and communication of results is sometimes unclear.
60%-69%: Struggling effort. Student is making some effort, but has misunderstanding of many concepts and is unable to put together a cogent argument. Communication of results is unclear.
Below 60%: Student is not making a sufficient effort.

Late work policy

There is no late work accepted on this project. Be sure to turn in your work early to avoid any technological mishaps.

Formatting + communication

Suppress Code, Warnings, & Messages

Include the following code in a code chunk at the top of your .Rmd file to suppress all code, warnings, and other messages. Use the code chunk header {r set-up, include = FALSE} to suppress this set up code.

knitr::opts_chunk$set(echo = FALSE,
                      warning = FALSE, 
                      message = FALSE)

Headers

Use headers to clearly label each section. Make sure there is a space between the last # and the title, so the header renders correctly. For example, ###Section Title will not render as header, but ### Section Title will.

References

Include all references in a section called “References” at the end of the report.
This course does not have specific requirements for formatting citations and references.
See Section 4.5 of the R Markdown Cookbook to learn about the citation functionality in R Markdown.

Appendix

If you have additional work that does not fit or does not belong in the body of the report, you may put it at the end of the document in section called “Appendix”.
The items in the appendix should be properly labeled.
The appendix should only be for additional material. The reader should be able to fully understand your report without viewing content in the appendix.

Resize figures

Resize plots and figures, so you have more space for the narrative.
- Resize individual figures: Use the code chunk header {r plot1, fig.height = 3, fig.width = 5}, replacing plot1 with a meaningful label and the height and width with values appropriate for your write up.
- Resize all figures: Include the fig_width and fig_height options in your YAML header as shown below:

---
title: "Your Title"
author: "Team Name + Group Members"
output: 
  pdf_document:
    fig_width: 5
    fig_height: 3
---

Replace the height and width values with values appropriate for your write up.

Arranging plots

Arrange plots in a grid, instead of one after the other. This is especially useful when displaying plots for exploratory data analysis and to check assumptions.

If you’re using ggplot2 functions, the patchwork package makes it easy to arrange plots in a grid. See the documentation and examples here.
If you’re using base R function, i.e. when using the emplogit functions, put the code par(mfrow = c(rows,columns)) before the code to make the plots. For example, par(mfrow = c(2,3)) will arrange plots in a grid with 2 rows and 3 columns.

Plot titles and axis labels

Be sure all plot titles and axis labels are visible and easy to read.

Use informative titles, not variable names, for titles and axis labels.
Use coord_flip() to flip the x and y axes on the plot. This is useful if you a bar plot with an x-axis that is difficult to read due to overlapping text.

❌ NO! The x-axis is hard to read because the names overlap.

ggplot(data = mpg, aes(x = manufacturer)) +
  geom_bar()

✅ YES! Names are readable

ggplot(data = mpg, aes(x = manufacturer)) +
  geom_bar() +
  coord_flip()

Do a little more to make the plot look professional!

Informative title and axis labels
Flipped coordinates to make names readable
Arranged bars based on count
Capitalized manufacturer names
Optional: Added color - Use a coordinated color scheme throughout paper / presentation
Optional: Applied a theme - Use same theme throughout paper / presentation

mpg %>%
  count(manufacturer) %>%
  mutate(manufacturer = str_to_title(manufacturer)) %>%
  ggplot(aes(x = fct_reorder(manufacturer,n), y = n)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  coord_flip() +
  labs(x = "Manufacturer", 
       y = "Count", 
       title = "The most common manufacturer is Dodge") +
  theme_bw()

Tables and model output

Use the kable function from the knitr package to neatly output all tables and model output. This will also ensure all model coefficients are displayed.
- Use the digits argument to display only 3 or 4 significant digits.
- Use the caption argument to add captions to your table.

model <- lm(mpg ~ hp, data = mtcars)
tidy(model) %>%
  kable(digits = 3)

term	estimate	std.error	statistic	p.value
(Intercept)	30.099	1.634	18.421	0
hp	-0.068	0.010	-6.742	0

Guidelines for communicating results

Don’t use variable names in your narrative! Use descriptive terms, so the reader understands your narrative without relying on the codebook.
- ❌ There is a negative linear relationship between mpg and hp.
- ✅ There is a negative linear relationship between a car’s fuel economy (in miles per gallon) and its horsepower.
Know your audience: Your report should be written for a general audience who has an understanding of statistics at the level of STA 210.
Avoid subject matter jargon: Don’t assume the audience knows all of the specific terminology related to your subject area. If you must use jargon, include a brief definition the first time you introduce a term.
Tell the “so what”: Your report and presentation should be more than a list of interpretations and technical definitions. Focus on what the results mean, i.e. what you want the audience to know about your topic after reading your report or viewing your presentation.
- ❌ For every one unit increase in horsepower, we expect the miles per gallon to decrease by 0.068 units, on average.
- ✅ If the priority is to have good fuel economy, then one should choose a car with lower horsepower. Based on our model, the fuel economy is expected to decrease, on average, by 0.68 miles per gallon for every 10 additional horsepower.
Tell a story: All visualizations, tables, model output, and narrative should tell a cohesive story!
Use one voice: Though multiple people are writing the report, it should read as if it’s from a single author. At least one team member should read through the report before submission to ensure it reads like a cohesive document.