This presentation demonstrates how statistical modeling and visualization can be applied to Formula 1–style performance data. Using a reproducible dataset, we analyze the relationship between lap time, average speed, and tire wear through regression techniques. The analysis is supported by ggplot visualizations, an interactive Plotly plot, and mathematical formulation to illustrate key assumptions and insights used in data-driven performance evaluation.

This presentation explores how statistical modeling can be applied to Formula 1 performance analysis. Using a simulated lap-time dataset, we examine the relationship between average speed, tire wear, and lap time through regression techniques and visualizations. Interactive and static plots are used to illustrate key patterns, model assumptions, and performance trade-offs commonly faced in motorsport engineering and data-driven decision making.