Understanding And Applying Basic Statistical Methods Using R

This book is an introductory statistics text that fills the tremendous gap between advances in the statistics literature and methods routinely used by most non-statisticians. While many books use R to explore more advanced topics, the introductory titles frequent lack any R programming, a shortcoming that this book addresses. It includes the expected topics in an introductory way, but unlike the typical book, modern insights about the relative merits of standard methods are discussed and methods for dealing with known problems associated with classic techniques are covered. The advances relevant to an introductory course include new insights related to the central limit theorem, advances on how to deal with heteroscedasticity (both ANOVA and regression), and better methods for dealing with both skewed distributions and outliers. The book also includes a primer on how to use R, and each chapter includes a description of how to apply both classic and more modern methods using R. Some sections focus on certain computational details to help the reader become acquainted with basic concepts and principles that are essential in terms of understanding the relatively merits of the many methods that are now available. In other sections, the computational details are kept to a minimum and the focus is on R. The book is designed for a one-semester statistics course being taken by students in other departments or practitioners looking to better understand statistics and R programming.