This paper conducts a statistical analysis to forecast Kamala Harris’s support rate in the 2024 U.S. presidential election. Utilizing a linear regression model, the study integrates past polling data and the Dow Jones Industrial Average (DJIA) to identify economic influences on voter support. The key variables include Harris’s support rate and DJIA values from 60 and 90 days prior, under the hypothesis that positive economic performance boosts support for the incumbent party. The analysis highlights the correlation between economic indicators and political outcomes, with visual tools and diagnostics to validate the model’s assumptions and predictions.
Strong Positive Points:
1. Comprehensive Data Simulation: The use of multiple R scripts, such as 00-simulate_data.R and 01-test_simulated_data.R, showcases a thorough approach to data generation and validation, ensuring reproducibility.
2. File Organization: The structure of files, from data downloading (02-download_data.R) to cleaning (03-clean_data.R) and modeling (06-model_data.R), demonstrates a logical workflow, aiding clarity and reproducibility.
3. Focus on Economic Indicators: Emphasizing DJIA as a predictor aligns with economic theories, providing a relevant basis for the model.
Critical Improvements Needed:
1. File Duplication and Plagiarism: The exploratory and replication files (05-exploratory_data_analysis.R and 07-replications.R) are identical to the example provided by the instructor. These should either be modified to reflect original analysis or removed entirely to avoid plagiarism and demonstrate original work.
2. Incomplete Paper Sections: The paper currently lacks a title and several sections remain unfinished. Completing these parts, especially the results and discussion sections, is essential for a coherent and polished final submission.
3. Proper Citations: The paper fails to cite R and the data sources used, which is a critical requirement. Omitting these citations will result in a zero for those components. Properly citing all tools and data sources is necessary to receive full credit.
4. LLM Usage Documentation: The file documenting LLM usage is not updated, and it currently does not comply with the project requirements, resulting in a zero. This needs to be revised to reflect the usage accurately.
5. Model Justification and Variable Details: While the linear model setup is mentioned, further explanation is needed regarding the choice of variables (e.g., why the DJIA values from 60 and 90 days were used) and assumptions. Providing this context will strengthen the model’s credibility.
6. Abstract and Title: The abstract is vague and does not provide sufficient details about the methodology or findings, and the paper lacks a title entirely. These are essential components that need to be added and refined.
Suggestions for Improvement:
1. Revise Duplicated Files: Modify the exploratory and replication files to reflect original work or develop new analyses that build on the instructor’s example while incorporating novel insights.
2. Complete All Sections: Ensure all parts of the paper, especially the title, results, and discussion sections, are fully developed. Summarize key findings clearly in the results and explore their implications in the discussion.
3. Update Citations: Properly cite all software (R), data sources, and any additional references used. This will not only avoid penalization but also demonstrate academic integrity and thoroughness.
4. Document LLM Usage: Update the LLM usage file to detail how AI was used in the project, ensuring transparency and compliance with the project criteria.
5. Expand the Abstract: Make the abstract more detailed and informative by clearly summarizing the key methods, findings, and significance of the study.
6. Adjust the Model Section: Provide more details in the model section about the selection of variables and the assumptions underlying the model. Discuss the potential limitations of these choices and explore other economic indicators to validate or contrast with the findings.
Evaluation:
• R is appropriately cited: 0/1 (needs to be cited correctly)
• Data are appropriately cited: 0/1 (needs to be cited correctly)
• Class project: 1/1
• LLM usage is documented: 0/1 (requires update)
• Title: 0/2 (missing entirely)
• Author, date, and repo: 2/2
• Abstract: 1/4 - Vague and insufficient detail
• Introduction: 3/4
• Estimand: 1/1
• Data: 7/10 - Clear, but lacks citation and diversity
• Measurement: 3/4
• Model: 5/10 - Needs more justification and detail
• Results: 4/10 - Minimal and needs expansion
• Discussion: 5/10 - Incomplete
• Prose: 4/6 - Clear but needs more engagement
• Cross-references: 1/1
• Captions: 2/2
• Graphs/Tables: 3/4 - Effective but could be expanded
• Idealized methodology: 5/10 - Requires more originality and clarity
• Referencing: 2/4 - Minor formatting issues and missing citations
• Commits: 2/2
• Sketches: 2/2
• Simulation: 3/3
• Tests-simulation: 3/4
• Tests-actual: 3/4
• Parquet: 1/1
• Reproducible workflow: 3/4
• Miscellaneous: 1/2
Estimated Overall Mark:
65 out of 100
Any Other Comments:
The project has a solid structure, but critical areas such as citations, abstract, and originality in file creation need improvement. Focus on refining these areas and developing all sections of the paper to enhance the overall quality. Good luck!
Summary
This paper conducts a statistical analysis to forecast Kamala Harris’s support rate in the 2024 U.S. presidential election. Utilizing a linear regression model, the study integrates past polling data and the Dow Jones Industrial Average (DJIA) to identify economic influences on voter support. The key variables include Harris’s support rate and DJIA values from 60 and 90 days prior, under the hypothesis that positive economic performance boosts support for the incumbent party. The analysis highlights the correlation between economic indicators and political outcomes, with visual tools and diagnostics to validate the model’s assumptions and predictions.
Strong Positive Points:
Critical Improvements Needed:
Suggestions for Improvement:
Evaluation:
Estimated Overall Mark:
65 out of 100
Any Other Comments:
The project has a solid structure, but critical areas such as citations, abstract, and originality in file creation need improvement. Focus on refining these areas and developing all sections of the paper to enhance the overall quality. Good luck!