Wearable fitness devices like Jawbone Up, Nike FuelBand, and Fitbit have revolutionized how individuals track their physical activities. While these tools excel at quantifying activity volume, they rarely assess execution quality. This project addresses this gap by analyzing accelerometer data collected from six participants performing barbell lifts.

This investigation explores the Central Limit Theorem through simulation of exponential distributions. We simulate 1,000 samples of size 40 from an exponential distribution with λ = 0.2, compute their means, and examine distribution properties. This report employs the "Epicycles of Analysis" framework by Peng and Matsui, which conceptualizes data analysis as iterative cycles of: Setting Expectations → Collecting Information → Comparing Expectations to Data → Refining Analysis. Each cycle builds upon the previous, creating a spiral of increasing understanding.

This investigation explores the Central Limit Theorem through simulation of exponential distributions. We simulate 1,000 samples of size 40 from an exponential distribution with λ = 0.2, compute their means, and examine distribution properties.

This analysis uses the NOAA Storm Events database (1950 through November 2011) to identify which event types cause the greatest harm to population health (fatalities + injuries) and which cause the largest economic damage (property + crop losses). The R Markdown document downloads the raw compressed CSV, performs reproducible processing (including interpreting damage exponent codes), summarizes the data by event type, and presents bar charts of the top contributors for both public-health and economic impact. Code is shown for every step so the analysis can be re-run and published to RPubs.