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. In Statistics Slam Dunk you'll develop a toolbox of R data skills including: Reading and writing data Installing and loading packages Transforming, tidying, and wrangling data Applying best-in-class exploratory data analysis techniques Creating compelling visualizations Developing supervised and unsupervised machine learning algorithms Execute hypothesis tests, including t-tests and chi-square tests for independence Compute expected values, Gini coefficients, and z-scores
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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology
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. About the book
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. You'll get practical experience cleaning, manipulating, exploring, testing, and otherwise analyzing data with base R functions and useful R packages. R's visualization capabilities shine through in the book's 300 visualizations, and almost 30<