zcyjn233
commented
1 week ago Summary This paper predicts the outcome of the 2024 U.S. Presidential Election between Kamala Harris and Donald Trump using multiple linear regression models based on polling data and state-level demographics.
Strong positive points: Incorporating state-level polling data enhances the precision and depth of the predictions. The paper is clearly written and provides detailed explanations throughout. The data cleaning process and visualizations are good.
Critical improvements needed: Accurate data source citation: The polling data must be properly cited both within the text and in the reference list to meet academic standards. LLM tool documentation: If any language models or AI tools were utilized, their usage must be properly documented in the designated file. Improved title: Lacks a meaningful and precise subtitle that reflects the main conclusions of the study. Including a more targeted subtitle will enhance the clarity of the paper's focus. Clearer explanation of estimands: A critical paragraph explaining the estimand and its importance is missing, which weakens the clarity of the research objective. Expand data interpretation: Data section should be expanded with more figures and thorough interpretations to fully explain the variables and findings.
Suggestions for improvement: There is still plenty of time to fix all the problems.My advice is to find the data first, then describe the data through charts, then complete the modeling, improve the discussion and introduction, and finally complete the abstract. Evaluation: R is appropriately cited(1/1): cite R correctly
Data are appropriately cited(0/1): No data source found
Class paper(0/1): not a class paper
LLM Usage is documented(0/1): Need to supplement LLM information
Title(0/2): do not have finding
Author, date, and repo(0/2):
Abstract(0/4)
Introduction(0/4):
Estimand(0/1): not written yet
Data(0/10):
Measurement(0/4): No description of how the poll was conducted
Model(0/10):
Results(0/10): Hope to get more explanation
Discussion(0/10): Provide more details on the weighting mechanism for different predictors.
Pross(0/6): well-done Pross
Cross-references(0/1): paper not written yet, so this is not possible either
Captions(0/2):
Graphs/tables/etc(0/4): no graphs/tables present yet
Idealized methodology(0/10):
Idealized survey(0/4): no idealized survey appendix present yet
Pollster method overview and evaluation (0/10): no pollster method appendix present yet
Referencing(0/4):
Commits(0/2):
Sketches(0/2):
Simulation(0/4):
Tests-simulation(0/4):
Tests - actual(0/4):
Parquet(0/1):
Reproducible workflow(0/4):
Miscellaneous(0/3):
Overall mark: (0/126)
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