yulexun
commented
4 weeks ago title, adbstract and readme will be added 11/3. Data cleaning updated, model and results added
Closed shamayla38 closed 4 weeks ago
yulexun
commented
4 weeks ago title, adbstract and readme will be added 11/3. Data cleaning updated, model and results added
Summary:
The paper "Prediction of US Election with Linear Model" by Colin Sihan Yang, Lexun Yu, and Siddharth Gowda presents an election forecasting model using polling data for the 2024 U.S. Presidential Election. The authors employ a "poll-of-polls" approach, aggregating polling data from FiveThirtyEight. They develop a linear regression model with predictors such as sample size, poll score, and the time gap from the election date. While the model aims to predict support for candidates Kamala Harris and Donald Trump, several sections remain incomplete, affecting the clarity and overall presentation of the work.
Strong positive points:
Introduction is clear at explaining what research gap this paper fills. Clearly mentions what the estimand is.
Critical improvements needed:
The paper is missing a proper abstract. This is crucial to help readers quickly understand the paper's objective, key findings, and relevance. There is no subtitle to convey the main findings of the paper (i.e., who the model predicts will win the popular vote or key insights). A more specific subtitle could enhance the paper’s clarity. The introduction section is missing crucial elements, including background information, detailed cross-referencing of sections, and more context on the relevance of the study. The readme of the paper is vague and needs more details of the project. The data section is incomplete and does not include graphs or tables that illustrate the dataset, and no reasons are given for choosing specific variables (poll reliability, sample size, poll score). Data cleaning does not cite relevant packages used for the cleaning process. Additionally, the model,results,appendix section is incomplete. Should be completed. Should remove irrelevant files from github.
Suggestions for improvement:
Add a concise and informative abstract: The abstract should briefly summarize the main objectives, findings, and relevance of the paper. This will help readers quickly understand the purpose and significance of the study. Update the subtitle to include key findings: Include a more specific subtitle that conveys the main insights from the paper, such as who the model predicts will win the popular vote and the key predictors used in the analysis. Expand the introduction: Add more background information and context to the introduction to explain why this research is important and how it fits into the broader field of election forecasting. Include cross-references to the sections of the paper to improve flow and clarity.
Enhance the README file: Provide more detailed information about the project, including an explanation of the methodology, a clear description of the data and model used, and instructions for replicating the analysis. This will make the project more accessible to readers.
Improve the data section: Include graphs or tables to illustrate the dataset and provide reasons for choosing specific variables, such as poll reliability, sample size, and poll score. This will help justify the choices made in the analysis. Cite relevant packages for data cleaning: Ensure that all packages used in the data cleaning process are properly cited, which will increase transparency and help with reproducibility.
Complete the model, results, and appendix sections: The model section should include discussions of assumptions and validation checks. The results section should present the findings in detail with appropriate visualizations. The appendix should be formatted properly and contain additional details such as diagnostics or extended data tables. Clean up the GitHub repository: Remove irrelevant files from the repository and ensure all commit messages are informative, explaining the changes made in each update. This will improve the organization and professionalism of the project.
Rubric:
R is appropriately cited: 1/1 Data are appropriately cited: 1/1 Class paper: 1/1 LLM usage is documented: 1/1 Title: 1/2 (Title is vague, and the subtitle does not mention the key findings) Author, date, and repo: 2/2 Abstract: 1/4 (Incomplete abstract provided) Introduction: 2/4 (Missing context and cross-referencing) Estimand: 1/1 Data: 4/10 (Missing graphs/tables and explanation of variable choices) Measurement: 2/4 (Basic explanation but lacking detailed discussion) Model: 0/10 (Model section lacks validation, assumptions, and proper discussion) Results: 0/10 (Results section is missing) Discussion: 0/10 (Placeholders in discussion; needs deeper analysis) Prose: 1/6 (Clear, but incomplete sections affect flow) Cross-references: 0/1 (Incomplete cross-references) Captions: 0/2 (No captions for graphs/tables) Graphs/tables/etc: 2/4 (Graphs lack proper formatting and captions) Idealized methodology: 0/10 Pollster methodology overview and evaluation: 0/10 Referencing: 1/4 (Properly cited) Commits: 1/2 (very few commits) Sketches: 2/2 Simulation: 3/4 Tests – simulation: 4/4 Tests – actual: 4/4 (No actual tests provided) Parquet: 0/1 (Data not in Parquet format) Reproducible workflow: 1/4 (Some steps missing for a fully reproducible workflow) Miscellaneous: 1/3 (Some effort shown, but incomplete sections limit the impact)
Mark (37/126)