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9781119061397 English 1119061393 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., 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.
9781119061397 English 1119061393 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., 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.