In the previous part of this project, I looked at the variables I was using, and some of the trends that I identified through a preliminary analysis. Part 2 of this project is dedicated to: - The research questions I formulated - The statistical analyses that I used for each question - The interpretation of my analysis - What conclusions I was able to draw to answer each research question Research Questions Based on my preliminary analysis of the variables that I was working with, I came up with more questions that I was interested in exploring, in addition to my original goal of figuring out whether the car or driver was more crucial to Formula 1 success. One of the first things that piqued my interest was how the different points systems affected overall scoring. While it was immediately clear that the change in point scoring systems from 10 points for a win to 25 points for a win resulted in drastic changes to the point totals, my hypothesis was that the change from sys
One of the most hotly debated topics in Formula 1 is over who should receive more of the plaudits for success, the car, or the driver. There are many views that range over the entire spectrum of opinions, from those who believe that the best car would win even with the worst driver, to those who think that the best driver can single-handedly drag a middling car to greatness. As a Formula 1 fan, I have always been very intrigued by this question. The goal of my analysis in the paper is to provide a quantitative look at this age-old question and set up a foundation upon which further empirical research into this topic can be conducted. This project is adapted from a project that I worked on for a college Statistics for Data Science class that I had taken. In this project, I looked at the last 30 years of Formula 1 data, spanning 1994 to 2023. I looked at various statistics that I thought would be insightful, and I eliminated metrics that were redundant and did not offer much avenue f