Summary:
This paper written by Mingrui Li presents a projection of how many votes Trump will get in 2024 compared to Harris. The linear regression model is used with three predictors. However, there is still room for improvement in the completeness of this paper.
Strong positive points:
The structure of LLM usage is great and fluent.
The model selection is appropriate and fits the data analysis well.
Critical improvements needed:
There should be some comments around the codes to explain and show the process.
The subtitle is missing a description of the main finding.
The result section is limited because the model is not completely explained.
The content of appendix is good, but the format is supposed to be corrected.
Suggestions for improvement:
It might be helpful if you make the model and the results more detailed.
The commits should be more clear to let us know what you updated.
The tittle should add the part of the result summary. In addition, the subtitle should add the main findings.
The format of the appendix should be modified.
The new reference should be in a new page.
evaluation:
R is appropriately cited (1 pts):
1/1: Has R referenced
Data are appropriately cited (1 pts):
1/1: Data is properly cited.
Class paper (1 pts):
1/1: No indication this is about a sta304 class project.
LLM usage documented (1 pts):
1/1: All things are clearly contained in README and usage.txt.
Title (2 pts):
1/2: The title could have main findings.
Author, date, and repo (2 pts):
1/2: Has author name, date, but do not have GitHub repo link.
Abstract (4 pts):
0/4: missing the abstract content.
Introduction (4 pts):
1/4: The introduction could be more complete and detailed.
Estimand (1 pts):
1/1: The percentage vote of estimand is stated completely.
Data (10 pts):
2/10: No summary for clean data. No graphs and summary statistics.
Measurement (4 pts):
3/4: Measurement is great but can be more detailed.
Model (10 pts):
2/10: It is lack of underlying assumptions.
Results (10 pts):
0/10: No Results.
Discussion (10 pts):
2/10: Discussion can be more and contains personal perspectives.
Prose (6 pts):
6/6: The prose is well done.
Cross-references (1 pts):
0/1: No Cross-references.
Captions (2 pts):
0/2: No Captions.
Graphs/tables/etc (4 pts):
0/4: No Graphs, tables.
Idealized methodology (10 pts):
9/10: The idealized methodology is great.
Idealized survey (4 pts):
4/4: The Idealized survey is completed.
Pollster methodology overview and evaluation (10 pts):
9/10: The overview is deep and comprehensive, with strengths and weaknesses discussed.
Referencing (4 pts):
4/4: well done.
Commits (2 pts):
1/2: Commits not clear.
Sketches (2 pts):
0/2: No Sketches.
Simulation (4 pts):
0/4: No Simulation.
Tests - simulation (4 pts):
0/4: No Tests.
Tests - actual (4 pts):
0/4: No Tests.
Parquet (1 pts):
0/1: incorrect format.
Reproducible workflow (4 pts):
0/4: The getting data path is from local.
Miscellaneous (3 pts):
0/3: Unfinished
Estimated overall mark:
50/126
other comments:
Although this paper needs to be more detailed and more complete, it still has room for improvement. I hope the author can make it better with deeper understanding of this data!
Summary: This paper written by Mingrui Li presents a projection of how many votes Trump will get in 2024 compared to Harris. The linear regression model is used with three predictors. However, there is still room for improvement in the completeness of this paper. Strong positive points:
Data are appropriately cited (1 pts): 1/1: Data is properly cited.
Class paper (1 pts): 1/1: No indication this is about a sta304 class project.
LLM usage documented (1 pts): 1/1: All things are clearly contained in README and usage.txt.
Title (2 pts): 1/2: The title could have main findings.
Author, date, and repo (2 pts): 1/2: Has author name, date, but do not have GitHub repo link.
Abstract (4 pts): 0/4: missing the abstract content.
Introduction (4 pts): 1/4: The introduction could be more complete and detailed.
Estimand (1 pts): 1/1: The percentage vote of estimand is stated completely.
Data (10 pts): 2/10: No summary for clean data. No graphs and summary statistics.
Measurement (4 pts): 3/4: Measurement is great but can be more detailed.
Model (10 pts): 2/10: It is lack of underlying assumptions.
Results (10 pts): 0/10: No Results.
Discussion (10 pts): 2/10: Discussion can be more and contains personal perspectives.
Prose (6 pts): 6/6: The prose is well done.
Cross-references (1 pts): 0/1: No Cross-references.
Captions (2 pts): 0/2: No Captions.
Graphs/tables/etc (4 pts): 0/4: No Graphs, tables.
Idealized methodology (10 pts): 9/10: The idealized methodology is great.
Idealized survey (4 pts): 4/4: The Idealized survey is completed.
Pollster methodology overview and evaluation (10 pts): 9/10: The overview is deep and comprehensive, with strengths and weaknesses discussed.
Referencing (4 pts): 4/4: well done.
Commits (2 pts): 1/2: Commits not clear.
Sketches (2 pts): 0/2: No Sketches.
Simulation (4 pts): 0/4: No Simulation.
Tests - simulation (4 pts): 0/4: No Tests.
Tests - actual (4 pts): 0/4: No Tests.
Parquet (1 pts): 0/1: incorrect format.
Reproducible workflow (4 pts): 0/4: The getting data path is from local.
Miscellaneous (3 pts): 0/3: Unfinished
Estimated overall mark: 50/126 other comments: Although this paper needs to be more detailed and more complete, it still has room for improvement. I hope the author can make it better with deeper understanding of this data!