ForewordThis book was, in part, written to be rigorously suitable for a sole mathematics course forundergraduates for what may be called “core.” In that respect, it may be suited to fit into astatistical methods course (usually with either an elementary or introductory modifier atthe front). Additionally, depending on how far through the last chapters one goes, it mayalso be suitable for an upper-division undergraduate course for the social sciences (e.g.,psychology or sociology).Beyond these objectives, however, this book is meant to empower learnersto physically experience statistical thinking through the hands-on use of the Rprogramming language. While theory will not be ignored, techniques and theory will befirst motivated through models, visuals, and an intuitive approach. The true objectiveis to share a language for communicating complex numerical facts in understandableterms. This practical application – sometimes called empirical and quantitativeskills – is designed to enable students to critically think and explore increasinglycomplex data sets, successfully describe and summarize large quantities of information,and accurately analyze, model, and communicate results to both technical and layaudiences.To make this happen, this text has two distinct parts. Part 1 is designed to efficientlywalk the reader step by step through installing and understanding the essentialminimums of the R programming language’s computer environment. We will do our bestto avoid “techno speak,” maintain an everyday language that jump-starts the reader asfast as possible through the initial stages, and move as smartly as possible to studyingactual statistics. Part 2 is a methods approach to introductory statistics. Populations,samples, descriptive statistics, probability, distributions, correlation, regression,confidence intervals, hypothesis testing, and analysis of variance (ANOVA) are all treatedin turn. While not shying away from technical mathematical theory, all statistical ideasare first introduced conceptually. From visualizations to hands-on activities to help “getyour hands dirty,” the goal is to build a solid, real-world, contextual intuition that makesthe theory more relatable. Your authors’ goal is to help learners “live a statistical life” –and completely avoid writing a book used to pass one course and then quickly forgotten.xxLastly, while we have said this text is suitable for an undergraduate course, it willwithout a doubt increase a learner’s ability to use a highly popular programminglanguage and lay the foundation for using R for research, data science, machine learning,dynamic reporting, and bespoke visualizations. As such, it is also suitable for practiceddata analysts looking to make the transition to R, for graduate students looking to bothgain a powerful skill and refresh their knowledge of statistics, and for anyone who enjoyslearning about data and statistics.Thank you for spending your time and attention on our book. Please be sure todownload source code and engage with this learning hands-on, and do not hesitate toreach out to us should anything not be working.
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