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Author: Tze-On Shawn Hui
Publisher:
ISBN:
Category :
Languages : en
Pages : 274
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Author: Tze-On Shawn Hui
Publisher:
ISBN:
Category :
Languages : en
Pages : 274
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Book Description
Author: Kai Yang
Publisher: McGraw Hill Professional
ISBN: 0071501371
Category : Technology & Engineering
Languages : en
Pages : 318
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Book Description
Multivariate statistical methods are an essential component of quality engineering data analysis. This monograph provides a solid background in multivariate statistical fundamentals and details key multivariate statistical methods, including simple multivariate data graphical display and multivariate data stratification. * Graphical multivariate data display * Multivariate regression and path analysis * Multivariate process control charts * Six sigma and multivariate statistical methods
Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 960
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Author: Dongseok Choi
Publisher: Springer
ISBN: 9811081689
Category : Mathematics
Languages : en
Pages : 168
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Book Description
This book presents the proceedings of the 2nd Pacific Rim Statistical Conference for Production Engineering: Production Engineering, Big Data and Statistics, which took place at Seoul National University in Seoul, Korea in December, 2016. The papers included discuss a wide range of statistical challenges, methods and applications for big data in production engineering, and introduce recent advances in relevant statistical methods.
Author: Chi-Yueh Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 344
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Book Description
Author: Brian Everitt
Publisher: Springer Science & Business Media
ISBN: 1441996508
Category : Mathematics
Languages : en
Pages : 284
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Book Description
The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.
Author: Institute for Operations Research and the Management Sciences. National Meeting
Publisher:
ISBN:
Category : Industrial management
Languages : en
Pages : 180
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Author:
Publisher:
ISBN:
Category : Computer integrated manufacturing systems
Languages : en
Pages : 1020
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Author: Patrick C. Hammett
Publisher:
ISBN:
Category :
Languages : en
Pages : 386
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Author: Dariusz J. Ceglarek
Publisher:
ISBN:
Category :
Languages : en
Pages : 416
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