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Features a straightforward and concise resource for introductory statistical concepts, methods, and techniques using R
Understanding and Applying Basic Statistical Methods Using R uniquely bridges the gap between advances in the statistical literature and methods routinely used by non–statisticians. Providing a conceptual basis for understanding the relative merits and applications of these methods, the book features modern insights and advances relevant to basic techniques in terms of dealing with non–normality, outliers, heteroscedasticity (unequal variances), and curvature.
Featuring a guide to R, the book uses R programming to explore introductory statistical concepts and standard methods for dealing with known problems associated with classic techniques. Thoroughly class–room tested, the book includes sections that focus on either R programming or computational details to help the reader become acquainted with basic concepts and principles essential in terms of understanding and applying the many methods currently available. Covering relevant material from a wide range of disciplines, Understanding and Applying Basic Statistical Methods Using R also includes:
Numerous illustrations and exercises that use data to demonstrate the practical importance of multiple perspectives
Discussions on common mistakes such as eliminating outliers and applying standard methods based on means using the remaining data
Detailed coverage on R programming with descriptions on how to apply both classic and more modern methods using R
A companion website with the data and solutions to all of the exercises
Understanding and Applying Basic Statistical Methods Using R is an ideal textbook for an undergraduate and graduate–level statistics courses in the science and/or social science departments. The book can also serve as a reference for professional statisticians and other practitioners looking to better understand modern statistical methods as well as R programming.
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