Statistics Every Programmer Needs teaches the nuts and bolts of applying statistics to the everyday problems you’ll face as a software developer. Each self-contained chapter provides a complete and comprehensive tutorial on a specific quantitative technique. Well-annotated and reusable Python code listings illustrate each method, with examples you can follow to practice your new skills.
You’ll predict ultramarathon split times using linear regression, identify raisin types from morphological features, forecast stock prices using time series models, analyze system reliability using Markov chains, and much more. You’ll not only learn how to use each method, but why it works, and how to explain your results. Whatever your field, you’ll soon be ready to model uncertainty, optimize resources, forecast outcomes, and assess risk with mathematical precision.
Is losing basketball games on purpose a rational strategy? Which hustle statistics have an impact on wins and losses? Does spending more on player salaries translate into a winning record? You’ll answer all these questions and more. Plus, R’s visualization capabilities shine through in the book’s 300 plots and charts, including Pareto charts, Sankey diagrams, Cleveland dot plots, and dendrograms.