For my Applied Statistics final project at Hanover College, my team and I analyzed the Pokémon Go Evolutions dataset using R. This project explored whether a Pokémon’s evolved combat power (CP_new) can be predicted using data available before evolution. We used R to clean and summarize the Pokémon Go dataset, created multiple visualizations for exploratory data analysis, and tested two regression models: 1. Simple Regression: CP_new predicted only by pre-evolution CP 2. Multiple Regression: Added HP, species, attack values, weight, and height The simple model explained most of the variation (adj. R squared ≈ 0.92), while the multiple model improved fit to adj. R squared ≈ 0.99. We also checked regression assumptions using diagnostic plots, which showed no major issues. Our results indicate that pre-evolution CP is the strongest predictor of evolved CP, and including other variables makes the model even stronger.