Learn statistics by analyzing professional basketball data! In this action-packed book, you'll build your skills in exploratory data analysis by digging into the fascinating world of NBA games and player stats using the R language. Statistics Slam Dunk upgrades your R data science skills by taking on practical analysis challenges based on NBA game and player data. Is losing games on purpose a rational strategy? Which hustle statistics have an impact on wins and losses? Each chapter in this one-of-a-kind guide uses new data science techniques to reveal interesting insights like these. And just like in the real world, you'll get no clean pre-packaged datasets in Statistics Slam Dunk. You'll take on the challenge of wrangling messy data to drill on the skills that will make you the star player on any data team. Amazing insights are hiding in raw data, and statistical analysis with R can help reveal them! R was built for data, and it supports modeling and statistical techniques including regression and classification models, time series forecasts, and clustering algorithms. And when you want to see your results, R's visualizations are stunning, with best-in-class plots and charts. Statistics Slam Dunk: Statistical analysis with R on real NBA data is an interesting and engaging how-to guide for statistical analysis using R. It's packed with practical statistical techniques, each demonstrated using real-world data taken from NBA games. In each chapter, you'll discover a new (and sometimes surprising!) insight into basketball, with careful step-by-step instructions on how to generate those revelations.