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This book presents a new augmentation method to eliminate the multicollinearity in datasets that contain several correlated predictor variables. The objective in mind is to reduce the estimation error of the regression coefficients. The main contribution of this work consists in offering a new alternative to eliminate multicollinearity in datasets by using small runs which are added in a sequential manner. The algorithm proposed will indicate the point in which the augmentations have sufficiently contributed to find the true regression model. The procedure is based on addition of new observations to the point in which an appropriate regression model can be constructed. The new information is obtained through designed experiments using the R3 algorithm as a guideline to perform the augmentations and the Ridge Trace and VIF statistic as verification tools that help to determine the point in which the correlations have been significantly reduced. The final result is a linear regression model that accurately represents the process under study.
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Informazioni sull?autore
M.I.E. Russell Pérez González received his I.E. from Instituto de Tuxtla Gutiérrez, Chiapas and his Masters from Technological Institute of Celaya, Guanajuato. M.I.E. Russell´s degrees are all in Industrial Engineering. His concentration area includes experimental design, mechanical design and technology management.
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A Revision on Multicollinearity and Augmentation Methods
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 3659507032
ISBN 13: 9783659507038
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A revision on Multicollinearity and Augmentation Methods
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ISBN 13: 9783659507038
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a new augmentation method to eliminate the multicollinearity in datasets that contain several correlated predictor variables. The objective in mind is to reduce the estimation error of the regression coefficients. The main contribution of this work consists in offering a new alternative to eliminate multicollinearity in datasets by using small runs which are added in a sequential manner. The algorithm proposed will indicate the point in which the augmentations have sufficiently contributed to find the true regression model. The procedure is based on addition of new observations to the point in which an appropriate regression model can be constructed. The new information is obtained through designed experiments using the R3 algorithm as a guideline to perform the augmentations and the Ridge Trace and VIF statistic as verification tools that help to determine the point in which the correlations have been significantly reduced. The final result is a linear regression model that accurately represents the process under study. 76 pp. Englisch. Codice articolo 9783659507038
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A Revision on Multicollinearity and Augmentation Methods
Editore:
VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 3659507032
ISBN 13: 9783659507038
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A Revision on Multicollinearity and Augmentation Methods
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2014
ISBN 10: 3659507032
ISBN 13: 9783659507038
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A revision on Multicollinearity and Augmentation Methods
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A revision on Multicollinearity and Augmentation Methods
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ISBN 13: 9783659507038
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Taschenbuch. Condizione: Neu. Neuware -This book presents a new augmentation method to eliminate the multicollinearity in datasets that contain several correlated predictor variables. The objective in mind is to reduce the estimation error of the regression coefficients. The main contribution of this work consists in offering a new alternative to eliminate multicollinearity in datasets by using small runs which are added in a sequential manner. The algorithm proposed will indicate the point in which the augmentations have sufficiently contributed to find the true regression model. The procedure is based on addition of new observations to the point in which an appropriate regression model can be constructed. The new information is obtained through designed experiments using the R3 algorithm as a guideline to perform the augmentations and the Ridge Trace and VIF statistic as verification tools that help to determine the point in which the correlations have been significantly reduced. The final result is a linear regression model that accurately represents the process under study.Books on Demand GmbH, Überseering 33, 22297 Hamburg 76 pp. Englisch. Codice articolo 9783659507038
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A revision on Multicollinearity and Augmentation Methods : Multicollinearity and Augmentation Methods
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ISBN 13: 9783659507038
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book presents a new augmentation method to eliminate the multicollinearity in datasets that contain several correlated predictor variables. The objective in mind is to reduce the estimation error of the regression coefficients. The main contribution of this work consists in offering a new alternative to eliminate multicollinearity in datasets by using small runs which are added in a sequential manner. The algorithm proposed will indicate the point in which the augmentations have sufficiently contributed to find the true regression model. The procedure is based on addition of new observations to the point in which an appropriate regression model can be constructed. The new information is obtained through designed experiments using the R3 algorithm as a guideline to perform the augmentations and the Ridge Trace and VIF statistic as verification tools that help to determine the point in which the correlations have been significantly reduced. The final result is a linear regression model that accurately represents the process under study. Codice articolo 9783659507038
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A revision on Multicollinearity and Augmentation Methods | Multicollinearity and Augmentation Methods
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LAP LAMBERT Academic Publishing, 2014
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ISBN 13: 9783659507038
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Taschenbuch. Condizione: Neu. A revision on Multicollinearity and Augmentation Methods | Multicollinearity and Augmentation Methods | Russell Pérez González (u. a.) | Taschenbuch | 76 S. | Englisch | 2014 | LAP LAMBERT Academic Publishing | EAN 9783659507038 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Codice articolo 105503646
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