Proposal feedback

[Gchism94]

Dear The Power of the Voters: Below is the score breakdown for your proposal. Details of things that should be updated/revised are provided in separate issues. As you address them, you should close the individual issues.

You can do this in one of two ways:

• On GitHub, by clicking on Close issue
• Via a commit that directly addresses the issue.

Please use the second method wherever a fix can be tied to a commit. If you preface your commit messages with "Fixes", "Fixed", "Fix", "Closed", or "Close", the issue will be closed when you push the changes to your repo. For example, suppose you want to close issue #2 which, hypothetically, suggested that you add a new line to the README, your commit message can say something like Add a new line to the README, closes #2.

Once you've closed all of the other issues, close this one as well, so that going into the presentation you have no open issues remaining.

	Available	Earned
Data	3	2
Proposal	5	3.5
Workflow	1	1
Teamwork	1	1
Total	10	7.5

Feedback: Data: I need to see the data type as well (integer, character, string, etc.) Teamwork: I can't see who is doing what, prospectively.

Proposal (overall, ChatGPT summarized my thoughts):

Your proposal for analyzing the US House Election Results dataset is quite comprehensive, with a well-thought-out plan for addressing your research questions. However, there are a few gaps and areas for improvement that could enhance the depth and accuracy of your analysis:

1. Missing Variables for Comprehensive Analysis:

• Population Data: You plan to merge population data, but there's no specific mention of how you'll handle variations in population data over time. Population dynamics can significantly influence voting patterns, and adjustments for population changes between census years should be considered.
• Demographic Information: Including demographic variables (e.g., age, race, income level) could provide deeper insights into voting trends and behaviors. Demographic shifts could have significant impacts on election outcomes and might explain changes in voting patterns.
• Economic and Social Data: Economic indicators, education levels, and other social factors at the district level could also influence voting patterns. These factors could provide additional context for your analysis, especially in understanding the broader reasons behind shifts in voting behavior.

2. Data Quality and Completeness:

• Handling Missing Data: There's no mention of how you plan to handle missing or incomplete data within your dataset. Considering strategies for dealing with missing values is crucial for maintaining the integrity of your analysis.
• Verification of Fusion Ticket and Write-in Data: The inclusion of fusion tickets and write-in candidates can complicate the analysis. Verifying the accuracy of these entries and understanding their impact on total votes are essential steps that should not be overlooked.

3. Analytical Depth and Methodology:

• Statistical Analysis Techniques: While you mention using regression analysis and chi-square tests, a more detailed explanation of your methodological approach could strengthen your proposal. For example, specifying the types of regression analysis or detailing how you plan to use these tests would provide clarity.
• Comparative Analysis Over Time: To address how US voting trends have changed over time, a more structured approach to time-series analysis might be needed. This could involve comparing voting patterns across multiple election cycles, considering external events that might influence trends, such as major political or social changes.

4. Project timeline:
   • I would have liked to see a timeline of some kind to help me see how feasible the project is in scope.

[Gchism94]

If you would like a regrade, please address and resubmit your proposal by Monday, Feb 19th, 11:59pm. Procedure

1. Close this issue.
2. Open a new blank one requesting a regrade

Your final score will be the midpoint between the two scores.

[sachinpatil3115]

Dear Professor ,

We have Updated Proposal with Suggested points :

1. Comprehensive Analysis: Mentioned data Limitation for Demographic Information
2. Data Quality and Completeness: Defined method to handle missing values
3. Analytical Depth and Methodology: Defined Statistical method
4. Project timeline - Defined 5 Week Roadmap for Project Completion