Hardcover. Etat : As New. No Jacket. 1st Edition. This is a fine, as new, hardcover first edition copy, no DJ, blue-yellow spine. This is an obviously unread copy that I got from the author. N� de r�f. du vendeur 095218

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Statistical Regression Modeling with R : Longitudinal and Multi-level Modeling

Jenny K. Chen, Ding-Geng (Din) Chen

Edit� par Springer International Publishing, 2021

ISBN 10 : 3030675823 ISBN 13 : 9783030675820

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Etat : Hervorragend. Zustand: Hervorragend | Seiten: 248 | Sprache: Englisch | Produktart: B�cher. N� de r�f. du vendeur 37342139/1

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Statistical Regression Modeling with R: Longitudinal and Multi-level Modeling (Emerging Topics in Statistics and Biostatistics)

Chen, Ding-Geng (Din); Chen, Jenny K.

Edit� par Springer, 2021

ISBN 10 : 3030675823 ISBN 13 : 9783030675820

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Statistical Regression Modeling with R: Longitudinal and Multi-level Modeling (Emerging Topics in Statistics and Biostatistics)

Chen, Ding-Geng (Din); Chen, Jenny K.

Edit� par Springer, 2021

ISBN 10 : 3030675823 ISBN 13 : 9783030675820

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Statistical Regression Modeling with R: Longitudinal and Multi-level Modeling (Emerging Topics in Statistics and Biostatistics)

Chen, Ding-Geng (Din); Chen, Jenny K.

Edit� par Springer, 2021

ISBN 10 : 3030675823 ISBN 13 : 9783030675820

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Statistical Regression Modeling with R

Jenny K. Chen

Edit� par Springer International Publishing, Springer Nature Switzerland Apr 2021, 2021

ISBN 10 : 3030675823 ISBN 13 : 9783030675820

Neuf Couverture rigide

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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear regression for normally distributed data, logistic regression for binomially distributed data, and Poisson regression and negative-binomial regression for count data. It then progresses to these regression models that work with longitudinal and multi-level data structures. The volume is designed to guide the transition from classical to more advanced regression modeling, as well as to contribute to the rapid development of statistics and data science. With data and computing programs available to facilitate readers' learning experience,Statistical Regression Modelingpromotes the applications of R in linear, nonlinear, longitudinal and multi-level regression. All included datasets, as well as the associated R program in packages nlme and lme4 for multi-level regression, are detailed in Appendix A. This book will be valuable in graduate courses on applied regression, as well as for practitioners and researchers in the fields of data science, statistical analytics, public health, and related fields. 248 pp. Englisch. N� de r�f. du vendeur 9783030675820

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Statistical Regression Modeling with R

Ding-Geng (Din) Chen|Jenny K. Chen

Edit� par Springer International Publishing, 2021

ISBN 10 : 3030675823 ISBN 13 : 9783030675820

Neuf Couverture rigide

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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Compiles commonly used regression methods that are essential for graduate students, applied data science, and relatedOffers a step-by-step implementation linear and multilevel regressions with normal and non-normal data and the application of R. N� de r�f. du vendeur 458552216

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Statistical Regression Modeling with R : Longitudinal and Multi-level Modeling

Jenny K. Chen

Edit� par Springer International Publishing, Springer Nature Switzerland, 2021

ISBN 10 : 3030675823 ISBN 13 : 9783030675820

Neuf Couverture rigide

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear regression for normally distributed data, logistic regression for binomially distributed data, and Poisson regression and negative-binomial regression for count data. It then progresses to these regression models that work with longitudinal and multi-level data structures. The volume is designed to guide the transition from classical to more advanced regression modeling, as well as to contribute to the rapid development of statistics and data science. With data and computing programs available to facilitate readers' learning experience,Statistical Regression Modelingpromotes the applications of R in linear, nonlinear, longitudinal and multi-level regression. All included datasets, as well as the associated R program in packages nlme and lme4 for multi-level regression, are detailed in Appendix A. This book will be valuable in graduate courses on applied regression, as well as for practitioners and researchers in the fields of data science, statistical analytics, public health, and related fields. N� de r�f. du vendeur 9783030675820

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