S. J. Welham, S. A. Gezan, S. J. Clark, A. Mead
Statistical Methods in Biology (eBook, ePUB)
Design and Analysis of Experiments and Regression
46,95 €
46,95 €
inkl. MwSt.
Sofort per Download lieferbar
23 °P sammeln
46,95 €
Als Download kaufen
46,95 €
inkl. MwSt.
Sofort per Download lieferbar
23 °P sammeln
Jetzt verschenken
Alle Infos zum eBook verschenken
46,95 €
inkl. MwSt.
Sofort per Download lieferbar
Alle Infos zum eBook verschenken
23 °P sammeln
S. J. Welham, S. A. Gezan, S. J. Clark, A. Mead
Statistical Methods in Biology (eBook, ePUB)
Design and Analysis of Experiments and Regression
- Format: ePub
- Merkliste
- Auf die Merkliste
- Bewerten Bewerten
- Teilen
- Produkt teilen
- Produkterinnerung
- Produkterinnerung
Bitte loggen Sie sich zunächst in Ihr Kundenkonto ein oder registrieren Sie sich bei
bücher.de, um das eBook-Abo tolino select nutzen zu können.
Hier können Sie sich einloggen
Hier können Sie sich einloggen
Sie sind bereits eingeloggt. Klicken Sie auf 2. tolino select Abo, um fortzufahren.
Bitte loggen Sie sich zunächst in Ihr Kundenkonto ein oder registrieren Sie sich bei bücher.de, um das eBook-Abo tolino select nutzen zu können.
Written in simple language with relevant examples, this illustrative introductory book presents best practices in experimental design and simple data analysis. Taking a practical and intuitive approach, it only uses mathematical formulae to formalize the methods where necessary and appropriate.
- Geräte: eReader
- ohne Kopierschutz
- eBook Hilfe
- Größe: 6.26MB
Andere Kunden interessierten sich auch für
- Paolo PostiglioneSpatial Econometric Methods in Agricultural Economics Using R (eBook, ePUB)43,95 €
- Roger MeadStatistical Methods in Agriculture and Experimental Biology (eBook, ePUB)101,95 €
- Weikai YanGGE Biplot Analysis (eBook, ePUB)61,95 €
- Reza HoshmandStatistical Methods for Environmental and Agricultural Sciences (eBook, ePUB)48,95 €
- Danny MccarrollSimple Statistical Tests for Geography (eBook, ePUB)56,95 €
- Catherine LegrandAdvanced Survival Models (eBook, ePUB)45,95 €
- Sustainable Agriculture for Food Security (eBook, ePUB)137,95 €
-
-
-
Written in simple language with relevant examples, this illustrative introductory book presents best practices in experimental design and simple data analysis. Taking a practical and intuitive approach, it only uses mathematical formulae to formalize the methods where necessary and appropriate.
Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis
- Erscheinungstermin: 22. August 2014
- Englisch
- ISBN-13: 9781040062326
- Artikelnr.: 72271101
- Verlag: Taylor & Francis
- Erscheinungstermin: 22. August 2014
- Englisch
- ISBN-13: 9781040062326
- Artikelnr.: 72271101
Suzanne Jane Welham obtained an MSc in statistical sciences from University College London in 1987 and worked as an applied statistician at Rothamsted Research from 1987 to 2000, collaborating with scientists and developing statistical software. She pursued a PhD from 2000 to 2003 at the London School of Hygiene and Tropical Medicine and then returned to Rothamsted, during which time she coauthored the in-house statistics courses that motivated the writing of this book. She is a coauthor of about 60 published papers and currently works for VSN International Ltd on the development of statistical software for analysis of linear mixed models and presents training courses on their use in R and GenStat.
Salvador Alejandro Gezan, PhD, is an assistant professor at the School of Forest Resources and Conservation at the University of Florida since 2011. Salvador obtained his bachelor's from the Universidad of Chile in forestry and his PhD from the University of Florida in statistics-genetics. He then worked as an applied statistician at Rothamsted Research, collaborating on the production and development of the in-house courses that formed the basis for this book. Currently, he teaches courses in linear and mixed model effects, quantitative genetics and forest mensuration. He carries out research and consulting in statistical application to biological sciences with emphasis on genetic improvement of plants and animals. Salvador is a long-time user of SAS, which he combines with GenStat, R and MATLAB as required.
Suzanne Jane Clark has worked at Rothamsted Research as an applied statistician since 1981. She primarily collaborates with ecologists and entomologists at Rothamsted, providing and implementing advice on statistical issues ranging from planning and design of experiments through to data analysis and presentation of results, and has coauthored over 130 scientific papers. Suzanne coauthored and presents several of the in-house st
Salvador Alejandro Gezan, PhD, is an assistant professor at the School of Forest Resources and Conservation at the University of Florida since 2011. Salvador obtained his bachelor's from the Universidad of Chile in forestry and his PhD from the University of Florida in statistics-genetics. He then worked as an applied statistician at Rothamsted Research, collaborating on the production and development of the in-house courses that formed the basis for this book. Currently, he teaches courses in linear and mixed model effects, quantitative genetics and forest mensuration. He carries out research and consulting in statistical application to biological sciences with emphasis on genetic improvement of plants and animals. Salvador is a long-time user of SAS, which he combines with GenStat, R and MATLAB as required.
Suzanne Jane Clark has worked at Rothamsted Research as an applied statistician since 1981. She primarily collaborates with ecologists and entomologists at Rothamsted, providing and implementing advice on statistical issues ranging from planning and design of experiments through to data analysis and presentation of results, and has coauthored over 130 scientific papers. Suzanne coauthored and presents several of the in-house st
Introduction. A Review of Basic Statistics. Principles for Designing
Experiments. Models for a Single Factor. Checking Model Assumptions.
Transformations of the Response. Models with Simple Blocking Structure.
Extracting Information about Treatments. Models with Complex Blocking
Structure. Replication and Power. Dealing with Non-Orthogonality. Models
for a Single Variate: Simple Linear Regression. Checking Model Fit. Models
for Several Variates: Multiple Linear Regression. Models for Variates and
Factors. Incorporating Structure: Mixed Models. Models for Curved
Relationships. Models for Non-Normal Responses: Generalized Linear Models.
Practical Design and Data Analysis for Real Studies. References.
Appendices.
Experiments. Models for a Single Factor. Checking Model Assumptions.
Transformations of the Response. Models with Simple Blocking Structure.
Extracting Information about Treatments. Models with Complex Blocking
Structure. Replication and Power. Dealing with Non-Orthogonality. Models
for a Single Variate: Simple Linear Regression. Checking Model Fit. Models
for Several Variates: Multiple Linear Regression. Models for Variates and
Factors. Incorporating Structure: Mixed Models. Models for Curved
Relationships. Models for Non-Normal Responses: Generalized Linear Models.
Practical Design and Data Analysis for Real Studies. References.
Appendices.
Introduction. A Review of Basic Statistics. Principles for Designing
Experiments. Models for a Single Factor. Checking Model Assumptions.
Transformations of the Response. Models with Simple Blocking Structure.
Extracting Information about Treatments. Models with Complex Blocking
Structure. Replication and Power. Dealing with Non-Orthogonality. Models
for a Single Variate: Simple Linear Regression. Checking Model Fit. Models
for Several Variates: Multiple Linear Regression. Models for Variates and
Factors. Incorporating Structure: Mixed Models. Models for Curved
Relationships. Models for Non-Normal Responses: Generalized Linear Models.
Practical Design and Data Analysis for Real Studies. References.
Appendices.
Experiments. Models for a Single Factor. Checking Model Assumptions.
Transformations of the Response. Models with Simple Blocking Structure.
Extracting Information about Treatments. Models with Complex Blocking
Structure. Replication and Power. Dealing with Non-Orthogonality. Models
for a Single Variate: Simple Linear Regression. Checking Model Fit. Models
for Several Variates: Multiple Linear Regression. Models for Variates and
Factors. Incorporating Structure: Mixed Models. Models for Curved
Relationships. Models for Non-Normal Responses: Generalized Linear Models.
Practical Design and Data Analysis for Real Studies. References.
Appendices.