What This Document Is
This document represents Mike Higgins’ first project for STAT 351, Business and Economic Statistics II at Kansas State University. It details a data cleaning and exploratory data analysis process performed on a dataset of stocks. The work is presented as R code and accompanying output, documenting the steps taken to prepare and initially investigate the data.
Why This Document Matters
This assignment is intended for students enrolled in STAT 351. It serves as a practical application of the statistical methods covered in the course, specifically focusing on data manipulation, cleaning, and basic descriptive statistics. It demonstrates a student’s ability to handle real-world data and prepare it for more advanced analysis.
Common Limitations or Challenges
This document is a student project and represents a single attempt at data analysis. It does not represent a comprehensive statistical study, nor does it offer definitive conclusions about the stock market. It focuses on the initial stages of analysis and does not include advanced modeling or forecasting.
What This Document Provides
The full document includes: code for data cleaning (handling missing values, creating new variables), descriptive statistics (sector means, standard deviations), correlation analysis, identification of top dividend and EPS performers, visualizations (histograms, scatterplots), and a demonstration of conditional variable creation. This preview does *not* include the complete R code, the full output of all analyses, or the complete cleaned dataset ("CleanStocks.csv").