Analysis on Muscle Recovery

Who Knew Doing Nothing Could Be So Productive?

The goal of this study was to examine the role of sleep in muscle recovery. This was done by leveraging insights from sleep deprivation and sleep extension studies. By exploring the relationships between sleep duration, hormonal levels (testosterone, cortisol, IGF-1), and recovery metrics, the project highlights how rest influences muscle repair.

Other than academic research, a key component of this analysis involved extensive Excel work. Using synthetic data generation techniques, I simulated realistic datasets based on research findings, incorporating variables like sleep duration, hormonal levels, and recovery rates. Functions such as RAND and NORM.INV were utilized to create a dataset with variability, ensuring normal distribution while reflecting real-world recovery conditions. Through correlation analysis, I was able to identify the strongest predictors of muscle recovery (Sleep duration, testosterone, and IGF-1). Furthering this, I conducted a multiple regression analysis to determine the combined influence of the variables on recovery showing that sleep duration and testosterone were the most significant contributors.

This project reflects my ability to design and execute complex data workflows. By synthesizing datasets based on academic research, performing statistical modeling and interpreting actionable results, I demonstrated how I can transform theoretical research into practical, realistic data-driven insights. Whether it's generating meaningful datasets or uncovering hidden trends, this work showcases my skillset in solving problems and delivering valuable insights using data.