In this GitHub repository, I share my hands-on experience with the Advanced Statistics Project, providing insights into the complexities of statistical analysis and its profound impact on real-world problem-solving. Additionally, I've ventured into Principal Component Analysis (PCA) on a dataset containing information about various colleges. Join me on this analytical journey as we unravel meaningful insights and draw business implications.
- Crafted hypotheses for one-way ANOVA on Salary concerning Education and Occupation.
- Executed one-way ANOVA on Salary for Education and Occupation separately, deciphering the results.
- Delved into the significance of class means if the null hypothesis was rejected.
- Explored the interaction between Education and Occupation using an engaging interaction plot.
- Conducted a two-way ANOVA on Salary, considering Education and Occupation (including their interaction).
- Provided insights into the business implications of the ANOVA results.
- Led an Exploratory Data Analysis (EDA) on college data, extracting valuable insights.
- Justified and executed scaling for PCA, comparing covariance and correlation matrices on scaled data.
- Unearthed eigenvalues and eigenvectors, performed PCA, and presented Principal Components.
- Offered a thoughtful interpretation of the business implications of using Principal Component Analysis.
This project has been an exhilarating journey of growth and learning. Navigating through the intricacies of statistical methods, from ANOVA to PCA, has not only honed my analytical skills but also deepened my understanding of their real-world applications. The blend of theoretical knowledge and hands-on implementation has truly been a game-changer.
To dive into the key findings, insights, and business implications of this project, check out the detailed Business Report provided alongside the Jupyter Notebooks. The Business Report, free from code but rich in explanations, holds the essence of the project's impact and recommendations for practical applications.
Feel free to explore the Jupyter Notebooks to dive into the nitty-gritty details of the code and analyses. Let's continue to explore the fascinating world of data together!