State the source of the data set.
Describe when and how it was originally collected (by the original data curator, not necessarily how you found the data)
Describe the observations and the general characteristics being measured in the data

Research question

Describe a research question you’re interested in answering using this data.

Glimpse of data

Use the glimpse function to provide an overview of each data set

Project proposal

The purpose of the project proposal is to help you think about your analysis strategy early.

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 how the data was originally collected (not how you found the data but how the original curator of the data collected it).

Section 3 - Analysis approach

In this section, you will provide a brief overview of your analysis approach. This includes:

Description of the response variable.
Visualization and summary statistics for the response variable.
List of variables that will be considered as predictors
Regression model technique (multiple linear regression and logistic regression)

Data dictionary (aka code book)

Submit a data dictionary for all the variables in your data set in the README of your project repo, in the data folder. Link to this file from your proposal writeup.

Submission

Push all of your final changes to the GitHub repo, and submit the PDF of your proposal to Gradescope.

Each component will be graded as follows:

Meets expectations (full credit): All required elements are completed and are accurate. The narrative is written clearly, all tables and visualizations are nicely formatted, and the work would be presentable in a professional setting.
Close to expectations (half credit): There are some elements missing and/or inaccurate. There are some issues with formatting.
Does not meet expectations (no credit): Major elements missing. Work is not neatly formatted and would not be presentable in a professional setting.

Draft report

The purpose of the draft and peer review is to give you an opportunity to get early feedback on your analysis. Therefore, the draft and peer review will focus primarily on the exploratory data analysis, modeling, and initial interpretations.

Write the draft in the written-report.qmd file in your project repo.

Below is a brief description of the sections to focus on in the draft:

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.

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.

Results

In this section, you will output the final model and include a brief discussion of the model assumptions, diagnostics, and any relevant model fit statistics.

This section also includes initial interpretations and conclusions drawn from the model.

Peer review

Critically reviewing others’ work is a crucial part of the scientific process, and STA 210 is no exception. Each lab team will be assigned two other teams’s projects to review. Each team should push their draft to their GitHub repo by the due date. One lab in the following week will be dedicated to the peer review, and all reviews will be due by the end of that lab session.

During the peer review process, you will be provided read-only access to your partner teams’ GitHub repos. Provide your review in the form of GitHub issues to your partner team’s GitHub repo using the issue template provided. The 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, modeling, interpretations, and conclusions.

Written report

Your written report must be completed in the written-report.qmd file and must be reproducible. All team members should 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.

You will submit the PDF of your final report on Gradescope.

The PDF you submit must match the files in 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 10 pages long. is no minimum page requirement; however, you should comprehensively address all of the analysis and report.

Be selective in what you include in your final write-up. The goal is to write a cohesive narrative that demonstrates a thorough and comprehensive analysis rather than explain every step of the analysis.

You are welcome to include an appendix with additional work at the end of the written report document; however, grading will largely be based on the content in the main body of the report. You should assume the reader will not see the material in the appendix unless prompted to view it in the main body of the report. The appendix should be neatly formatted and easy for the reader to navigate. It is not included in the 10-page limit.

The written report is worth 40 points, broken down as follows

Total	40 pts
Introduction/data	6 pts
Methodology	10 pts
Results	14 pts
Discussion + conclusion	6 pts
Organization + formatting	4 pts

Click here for a PDF of the written report rubric.

Introduction and data

Grading criteria

The research question and motivation are clearly stated in the introduction, including citations for the data source and any external research. The data are clearly described, including a description about how the data were originally collected and a concise definition of the variables relevant to understanding the report. The data cleaning process is clearly described, including any decisions made in the process (e.g., creating new variables, removing observations, etc.) The explanatory data analysis helps the reader better understand the observations in the data along with interesting and relevant relationships between the variables. It incorporates appropriate visualizations and summary statistics.

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, interactions considered, variable transformations (if needed), assessment of conditions and diagnostics, and any other relevant considerations that were part of the model fitting process.

Grading criteria

The analysis steps are appropriate for the data and research question. The group used a thorough and careful approach to select the final model; the approach is clearly described in the report. The model selection process took into account potential interaction effects and addressed any violations in model conditions. The model conditions and diagnostics are thoroughly and accurately assessed for their model. If violations of model conditions are still present, there was a reasonable attempt to address the violations based on the course content.

Results

This is where you will output the final model with any relevant model fit statistics.

Describe 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 using the model output to address the research questions, using the interpretations to support your conclusions. Focus on the variables that help you answer the research question and that provide relevant context for the reader.

Grading criteria

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 thoroughly assessed.

Discussion + Conclusion

In this section you’ll include a summary of what you have learned about your research question along with statistical arguments supporting your conclusions. In addition, discuss the limitations of your analysis and provide suggestions on ways the analysis could be improved. Any potential issues pertaining to the reliability and validity of your data and appropriateness of the statistical analysis should also be discussed here. Lastly, this section will include ideas for future work.

Grading criteria

Overall conclusions from analysis are clearly described, and the model results are put into the larger context of the subject matter and original research question. There is thoughtful consideration of potential limitations of the data and/or analysis, and ideas for future work are clearly described.

Organization + formatting

This is an assessment of the overall presentation and formatting of the written report.

Grading criteria

The report neatly written and organized with clear section headers and appropriately sized figures with informative labels. Numerical results are displayed with a reasonable number of digits, and all visualizations are neatly formatted. All citations and links are properly formatted. If there is an appendix, it is reasonably organized and easy for the reader to find relevant information. All code, warnings, and messages are suppressed. The main body of the written report (not including the appendix) is no longer than 10 pages.

Video presentation + slides

Slides

In addition to the written report, your team will also create presentation slides and record a video presentation that summarize and showcase your project. Introduce your research question and data set, showcase visualizations, and discuss the primary conclusions. These slides should serve as a brief visual addition to your written report and will be graded for content and quality.

For submission, convert these slides to a .pdf document, and submit the PDF of the slides on Gradescope.

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

Reproducibility + organization

All written work (with exception of presentation slides) should be reproducible, and the GitHub repo should be neatly organized.

The GitHub repo should have the following structure:

README: Short project description and data dictionary
written-report.qmd & written-report.pdf: Final written report
/data: Folder that contains the data set for the final project.
/previous-work: Folder that contains the topic-ideas and project-proposal files.
/presentation: Folder with the presentation slides.
- If your presentation slides are online, you can put a link to the slides in a README.md file in the presentation folder.

Points for reproducibility + organization will be based on the reproducibility of the written report and the organization of the project GitHub repo. The repo should be neatly organized as described above, there should be no extraneous files, all text in the README should be easily readable.

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.

If you have concerns with the teamwork and/or contribution from any team members, please email me by the project video deadline. You only need to email me if you have concerns. Otherwise, I will assume everyone on the team equally contributed and will receive full credit for the teamwork portion of the grade.

Overall grading

The grade breakdown is as follows:

Total	100 pts
Topic ideas
Project proposal
Peer review
Written report
Slides + video presentation
Reproducibility + organization
Video comments
Peer teamwork evaluation

Note: No late project reports or videos are accepted.

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.