This project aims to analyze data related to the 2024 election and make predictions based on voter behavior and demographic factors. Using statistical methods, we seek to understand the influences that shape voting preferences and provide insights into potential election outcomes.
The dataset used for this project was collected from [Class survey]. It is an observational study that includes demographic information and polling data. The data is available in CSV format in the repository.
we will analyze the data using various statistical methods, starting with correlation coefficients and linear regression to explore relationships between variables. We will conduct hypothesis testing, including a Z-test for proportions, to assess statistical significance, and perform normality and assumption tests like the F-test and Chi-Square. The results will be presented through numerical and graphical analyses, providing insights and predictions based on the data.
The analysis revealed several key insights from the data. The correlation and linear regression analyses highlighted the strength and direction of relationships between variables, while the Z-test for proportions indicated whether observed differences were statistically significant. Normality testing and assumption checks, such as the F-test and Chi-Square test, confirmed the validity of the statistical methods used, with results visualized through graphs and numerical summaries, leading to meaningful conclusions and predictions.
All R code used for data analysis and modeling is included in the repository.
To run the analysis:
- Clone the repository:
- Open the R scripts in RStudio.
- Ensure the necessary R packages are installed.
This project is licensed under the MIT License, allowing you to copy, modify, and distribute the work, even for commercial purposes, without asking permission.