Univariate, Bivariate, and Multivariate Statistics Using R - by Daniel J Denis (Hardcover)

About the Book

"This book provides a user-friendly and practical guide on R, with emphasis on covering a broader range of statistical methods than previous books on R. This is a "how to" book and will be of use to undergraduates and graduate students along with researchers and professionals who require a quick go-to source to help them perform essential statistical analyses and data management tasks in R. The book only assumes minimal prior knowledge of statistics, providing readers with the tools they need right now to help them understand and interpret their data analyses. This book covers univariate, bivariate, and multivariate statistical methods, as well as some nonparametric tests. It provides students with a hands-on easy-to-read manual on the wealth of applied statistics and essential R computing that they will need for their theses, dissertations, and research publications. A strength of this book is its scope of coverage of univariate through to multivariate procedures, while simultaneously serving as a friendly introduction to R software"--

Book Synopsis

A practical source for performing essential statistical analyses and data management tasks in R

Univariate, Bivariate, and Multivariate Statistics Using R offers a practical and very user-friendly introduction to the use of R software that covers a range of statistical methods featured in data analysis and data science. The author-- a noted expert in quantitative teaching --has written a quick go-to reference for performing essential statistical analyses and data management tasks in R. Requiring only minimal prior knowledge, the book introduces concepts needed for an immediate yet clear understanding of statistical concepts essential to interpreting software output.

The author explores univariate, bivariate, and multivariate statistical methods, as well as select nonparametric tests. Altogether a hands-on manual on the applied statistics and essential R computing capabilities needed to write theses, dissertations, as well as research publications. The book is comprehensive in its coverage of univariate through to multivariate procedures, while serving as a friendly and gentle introduction to R software for the newcomer. This important resource:

• Offers an introductory, concise guide to the computational tools that are useful for making sense out of data using R statistical software
• Provides a resource for students and professionals in the social, behavioral, and natural sciences
• Puts the emphasis on the computational tools used in the discovery of empirical patterns
• Features a variety of popular statistical analyses and data management tasks that can be immediately and quickly applied as needed to research projects
• Shows how to apply statistical analysis using R to data sets in order to get started quickly performing essential tasks in data analysis and data science

Written for students, professionals, and researchers primarily in the social, behavioral, and natural sciences, Univariate, Bivariate, and Multivariate Statistics Using R offers an easy-to-use guide for performing data analysis fast, with an emphasis on drawing conclusions from empirical observations. The book can also serve as a primary or secondary textbook for courses in data analysis or data science, or others in which quantitative methods are featured.

From the Back Cover

A practical source for performing essential statistical analyses and data management tasks in R

Univariate, Bivariate, and Multivariate Statistics Using R offers a practical and very user-friendly introduction to the use of R software that covers a range of statistical methods featured in data analysis and data science. The author-- a noted expert in quantitative teaching --has written a quick go-to reference for performing essential statistical analyses and data management tasks in R. Requiring only minimal prior knowledge, the book introduces concepts needed for an immediate yet clear understanding of statistical concepts essential to interpreting software output.

The author explores univariate, bivariate, and multivariate statistical methods, as well as select nonparametric tests. Altogether, a hands-on manual on the applied statistics and essential R computing capabilities needed to write theses, dissertations, as well as research publications. The book is comprehensive in its coverage of univariate through to multivariate procedures, while serving as a friendly and gentle introduction to R software for the newcomer. This important resource:

• Offers an introductory, concise guide to the computational tools that are useful for making sense out of data using R statistical software
• Provides a resource for students and professionals in the social, behavioral, and natural sciences
• Puts the emphasis on the computational tools used in the discovery of empirical patterns
• Features a variety of popular statistical analyses and data management tasks that can be immediately and quickly applied as needed to research projects
• Shows how to apply statistical analysis using R to data sets in order to get started quickly performing essential tasks in data analysis and data science

Written for students, professionals, and researchers primarily in the social, behavioral, and natural sciences, Univariate, Bivariate, and Multivariate Statistics Using R offers an easy-to-use guide for performing data analysis fast, with an emphasis on drawing conclusions from empirical observations. The book can also serve as a primary or secondary textbook for courses in data analysis or data science, or others in which quantitative methods are featured.

About the Author

DANIEL J. DENIS, PHD, is Professor of Quantitative Psychology in the Department of Psychology at the University of Montana. D. Denis is the author of Applied Univariate, Bivariate, and Multivariate Statistics and SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics, both published by Wiley.