2018
DOI: 10.1201/9780203749999
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Introduction to Multivariate Analysis

Abstract: The paperback edition is sold subject to the condition that it shall not, by way of trade or otherwise, be lent, re-sold, hired out, or otherwise circulated without the publisher's prior consent in any form of binding or cover other than that in which it is published and without a similar condition including this condition being imposed on the subsequent purchaser.All rights reserved. No part of this book may be reprinted, or reproduced or utilized in any form or by any electronic, mechanicalor other means, no… Show more

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Cited by 189 publications
(182 citation statements)
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“…In the principal components analysis of the seven explanatory variables, three components had eigenvalues in excess of unity and explained 50, 21 and 18% of the variation in the data, respectively. These three axes were subsequently rotated with the varimax rotation (Chatfield & Collins, ; Cooley & Lohnes, ).…”
Section: Resultsmentioning
confidence: 99%
“…In the principal components analysis of the seven explanatory variables, three components had eigenvalues in excess of unity and explained 50, 21 and 18% of the variation in the data, respectively. These three axes were subsequently rotated with the varimax rotation (Chatfield & Collins, ; Cooley & Lohnes, ).…”
Section: Resultsmentioning
confidence: 99%
“…In our work, the Spearman’s rank correlation is specifically used to assess the association between biogeochemical markers like dissolved oxygen and nitrate and hydrologic indicators such as hydraulic gradient, specific conductivity, and temperature. A Spearman’s rank correlation matrix [ Chatfield , 2018] is created to summarized these relationships.…”
Section: Methodsmentioning
confidence: 99%
“…). Principal components analysis (PCA: Chatfield and Collins ) was then used on transformed variables, normalized to have a mean of zero and a standard deviation of one, as a data reduction technique to identity orthogonal factors and the variables that loaded highly in each factor. We performed a nonmetric, multivariate, multidimensional scaling (NMDS: Kruskal and Wish , R package vegan v2.3‐1; Oksanen et al.…”
Section: Methodsmentioning
confidence: 99%