Rob Verdooren – författare

Instructs readers on how to use methods of statistics and experimental design with R software Applied statistics covers both the theory and the application of modern statistical and mathematical modelling techniques to applied problems in industry, public services, commerce, and research. It proceeds from a strong theoretical background, but it is practically oriented to develop one's ability to tackle new and non-standard problems confidently. Taking a practical approach to applied statistics, this user-friendly guide teaches readers how to use methods of statistics and experimental design without going deep into the theory.Applied Statistics: Theory and Problem Solutions with R includes chapters that cover R package sampling procedures, analysis of variance, point estimation, and more. It follows on the heels of Rasch and Schott's Mathematical Statistics via that book's theoretical background—taking the lessons learned from there to another level with this book’s addition of instructions on how to employ the methods using R. But there are two important chapters not mentioned in the theoretical back ground as Generalised Linear Models and Spatial Statistics.  Offers a practical over theoretical approach to the subject of applied statisticsProvides a pre-experimental as well as post-experimental approach to applied statisticsFeatures classroom tested materialApplicable to a wide range of people working in experimental design and all empirical sciencesIncludes 300 different procedures with R and examples with R-programs for the analysis and for determining minimal experimental sizesApplied Statistics: Theory and Problem Solutions with R will appeal to experimenters, statisticians, mathematicians, and all scientists using statistical procedures in the natural sciences, medicine, and psychology amongst others.

Instructs readers on how to use methods of statistics and experimental design with R software 

Applied statistics covers both the theory and the application of modern statistical and mathematical modelling techniques to applied problems in industry, public services, commerce, and research. It proceeds from a strong theoretical background, but it is practically oriented to develop one''s ability to tackle new and non-standard problems confidently. Taking a practical approach to applied statistics, this user-friendly guide teaches readers how to use methods of statistics and experimental design without going deep into the theory.

Applied Statistics: Theory and Problem Solutions with R includes chapters that cover R package sampling procedures, analysis of variance, point estimation, and more. It follows on the heels of Rasch and Schott''s Mathematical Statistics via that book''s theoretical background—taking the lessons learned from there to another level with this book’s addition of instructions on how to employ the methods using R. But there are two important chapters not mentioned in the theoretical back ground as Generalised Linear Models and Spatial Statistics. 

Offers a practical over theoretical approach to the subject of applied statistics Provides a pre-experimental as well as post-experimental approach to applied statistics Features classroom tested material Applicable to a wide range of people working in experimental design and all empirical sciences Includes 300 different procedures with R and examples with R-programs for the analysis and for determining minimal experimental sizes

Applied Statistics: Theory and Problem Solutions with R will appeal to experimenters, statisticians, mathematicians, and all scientists using statistical procedures in the natural sciences, medicine, and psychology amongst others. 

Instructs readers on how to use methods of statistics and experimental design with R software 

Applied statistics covers both the theory and the application of modern statistical and mathematical modelling techniques to applied problems in industry, public services, commerce, and research. It proceeds from a strong theoretical background, but it is practically oriented to develop one''s ability to tackle new and non-standard problems confidently. Taking a practical approach to applied statistics, this user-friendly guide teaches readers how to use methods of statistics and experimental design without going deep into the theory.

Applied Statistics: Theory and Problem Solutions with R includes chapters that cover R package sampling procedures, analysis of variance, point estimation, and more. It follows on the heels of Rasch and Schott''s Mathematical Statistics via that book''s theoretical background—taking the lessons learned from there to another level with this book’s addition of instructions on how to employ the methods using R. But there are two important chapters not mentioned in the theoretical back ground as Generalised Linear Models and Spatial Statistics. 

Offers a practical over theoretical approach to the subject of applied statistics Provides a pre-experimental as well as post-experimental approach to applied statistics Features classroom tested material Applicable to a wide range of people working in experimental design and all empirical sciences Includes 300 different procedures with R and examples with R-programs for the analysis and for determining minimal experimental sizes

Applied Statistics: Theory and Problem Solutions with R will appeal to experimenters, statisticians, mathematicians, and all scientists using statistical procedures in the natural sciences, medicine, and psychology amongst others. 

Dieses Buch führt in die angewandte Statistik für Agrarwissenschaften ein und unterstützt bei der Forschung in der Pflanzen- und Tierproduktion und im Feldversuchswesen. Es nutzt ausgiebig das frei verfügbare Programmpaket R: Über das gesamte Buch hinweg werden im Rahmen umfangreicher Beispiele passende R-Programmcodes angegeben und erläutert. Die Codes können mit eigenen Daten kombiniert und so zur Planung und Auswertung eigener Versuche verwendet werden. So können etwa Wachstumsfunktionen angepasst, Varianzanalysen berechnet oder optimale Versuchspläne und minimale Stichprobenumfänge gefunden werden und vieles mehr. Eine Installationsbeschreibung für R wird ebenfalls zur Verfügung gestellt. Zahlreiche Übungsaufgaben mit Lösungen ergänzen das Buch, so dass es als Lehr- und Nachschlagewerk nutzbar ist.Besonders hervorzuheben ist, dass auch balancierte unvollständige Blockanlagen (BUB) erläutert werden und erstmalig eine vollständige Liste kleinster (mit möglichst geringer Anzahl von Blocks) BUB für bis zu v = 25 Behandlungen und Blockgrößen ≤ v/2 im Netz zur Verfügung gestellt wird. Für Sortenversuche sind die BUB allerdings oft nicht nutzbar, weil sie zu viele Wiederholungen erfordern. Dafür haben sich „verallgemeinerte Gitter“ oder alpha-Anlagen bewährt, die ebenfalls behandelt werden.