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Add to basketPaperback. Condition: new. Paperback. Third-variable effect refers to the effect transmitted by third-variables that intervene in the relationship between an exposure and a response variable. Differentiating between the indirect effect of individual factors from multiple third-variables is a constant problem for modern researchers.Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS introduces general definitions of third-variable effects that are adaptable to all different types of response (categorical or continuous), exposure, or third-variables. Using this method, multiple third- variables of different types can be considered simultaneously, and the indirect effect carried by individual third-variables can be separated from the total effect. Readers of all disciplines familiar with introductory statistics will find this a valuable resource for analysis.Key Features:Parametric and nonparametric method in third variable analysisMultivariate and Multiple third-variable effect analysisMultilevel mediation/confounding analysisThird-variable effect analysis with high-dimensional data Moderation/Interaction effect analysis within the third-variable analysisR packages and SAS macros to implement methods proposed in the book This book introduces general definitions of third-variable effects that are adaptable to all different types of response (categorical or continuous), exposure, or third-variables. Readers of all disciplines familiar with introductory statistics will find this a valuable resource for analysis. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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ISBN 10: 0367365472 ISBN 13: 9780367365479
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Published by Chapman and Hall/CRC, 2024
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Third-variable effect refers to the effect transmitted by third-variables that intervene in the relationship between an exposure and a response variable. Differentiating between the indirect effect of individual factors from multiple third-variables is a constant problem for modern researchers.Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS introduces general definitions of third-variable effects that are adaptable to all different types of response (categorical or continuous), exposure, or third-variables. Using this method, multiple third- variables of different types can be considered simultaneously, and the indirect effect carried by individual third-variables can be separated from the total effect. Readers of all disciplines familiar with introductory statistics will find this a valuable resource for analysis.Key Features:Parametric and nonparametric method in third variable analysisMultivariate and Multiple third-variable effect analysisMultilevel mediation/confounding analysisThird-variable effect analysis with high-dimensional data Moderation/Interaction effect analysis within the third-variable analysisR packages and SAS macros to implement methods proposed in the book 294 pp. Englisch.
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Qingzhao Yu is Professor in Biostatistics, Louisiana State University Health Sciences Center.Bin Li is Associate Professor in Statistics, Louisiana State University.Third-variable effect refers to the effect transmitted by third-variabl.