β Scribed by Randall E. Schumacker
No coin nor oath required. For personal study only.
Using R with Multivariate Statistics by Randall E. Schumacker is a quick guide to using R, free-access software available for Windows and Mac operating systems that allows users to customize statistical analysis. Designed to serve as a companion to a more comprehensive text on multivariate statistics, this book helps students and researchers in the social and behavioral sciences get up to speed with using R. It provides data analysis examples, R code, computer output, and explanation of results for every multivariate statistical application included. In addition, R code for some of the data set examples used in more comprehensive texts is included, so students can run examples in R and compare results to those obtained using SAS, SPSS, or STATA. A unique feature of the book is the photographs and biographies of famous persons in the field of multivariate statistics.
Multivariate Statistics, R (Programming Language)
π SIMILAR VOLUMES
University of Plymouth, 2009. β 189 p. β ISBN: N/A<div class="bb-sep"></div>The objective of Multivariate Statistics with R is to cover a basic core of multivariate material in such a way that the core mathematical principles are covered, and to provide access to current applications and development
<p>This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program<b> R</b>, Professor Zelterman demonstrates the process and outcomes for a wide array
<p><p>This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program<b> R</b>, Professor Zelterman demonstrates the process and outcomes for a wide ar
<p>βββββThe intensive use of automatic data acquisition system and the use of cloud computing for process monitoring have led to an increased occurrence of industrial processes that utilize statistical process control and capability analysis. These analyses are performed almost exclusively with mult