Dieter Rasch – författare

Explores mathematical statistics in its entirety—from the fundamentals to modern methods

This book introduces readers to point estimation, confidence intervals, and statistical tests. Based on the general theory of linear models, it provides an in-depth overview of the following: analysis of variance (ANOVA) for models with fixed, random, and mixed effects; regression analysis is also first presented for linear models with fixed, random, and mixed effects before being expanded to nonlinear models; statistical multi-decision problems like statistical selection procedures (Bechhofer and Gupta) and sequential tests; and design of experiments from a mathematical-statistical point of view. Most analysis methods have been supplemented by formulae for minimal sample sizes. The chapters also contain exercises with hints for solutions.

Translated from the successful German text, Mathematical Statistics requires knowledge of probability theory (combinatorics, probability distributions, functions and sequences of random variables), which is typically taught in the earlier semesters of scientific and mathematical study courses. It teaches readers all about statistical analysis and covers the design of experiments. The book also describes optimal allocation in the chapters on regression analysis. Additionally, it features a chapter devoted solely to experimental designs.

Mathematical Statistics is a recommended text for advanced students and practitioners of math, probability, and statistics.

Explores mathematical statistics in its entirety—from the fundamentals to modern methods

This book introduces readers to point estimation, confidence intervals, and statistical tests. Based on the general theory of linear models, it provides an in-depth overview of the following: analysis of variance (ANOVA) for models with fixed, random, and mixed effects; regression analysis is also first presented for linear models with fixed, random, and mixed effects before being expanded to nonlinear models; statistical multi-decision problems like statistical selection procedures (Bechhofer and Gupta) and sequential tests; and design of experiments from a mathematical-statistical point of view. Most analysis methods have been supplemented by formulae for minimal sample sizes. The chapters also contain exercises with hints for solutions.

Translated from the successful German text, Mathematical Statistics requires knowledge of probability theory (combinatorics, probability distributions, functions and sequences of random variables), which is typically taught in the earlier semesters of scientific and mathematical study courses. It teaches readers all about statistical analysis and covers the design of experiments. The book also describes optimal allocation in the chapters on regression analysis. Additionally, it features a chapter devoted solely to experimental designs.

Mathematical Statistics is a recommended text for advanced students and practitioners of math, probability, and statistics.

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. 

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. 

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. 

The book looks at the process of empirical research into the following seven stages:

The book looks at the process of empirical research into the following seven stages:

This volume features original contributions and invited review articles on mathematical statistics, statistical simulation and experimental design. The selected peer-reviewed contributions originate from the 8th International Workshop on Simulation held in Vienna in 2015. The book is intended for mathematical statisticians, Ph.D. students and statisticians working in medicine, engineering, pharmacy, psychology, agriculture and other related fields.

The International Workshops on Simulation are devoted to statistical techniques in stochastic simulation, data collection, design of scientific experiments and studies representing broad areas of interest. The first 6 workshops took place in St. Petersburg, Russia, in 1994 – 2009 and the 7th workshop was held in Rimini, Italy, in 2013.

Die Verfahrensbibliothek Versuchsplanung und -auswertung ist eine umfangreiche Sammlung von fast 500 modernen und klassischen statistischen Methoden auf rund 2000 Seiten an deren Erarbeitung über 60 Wissenschaftler aus drei Erdteilen mitgewirkt haben. Neben rein methodischen Verfahren, in denen Tests, Konfidenz- und Punktschätzungen bzw. Regressions- und Varianzanalysen beschrieben werden, findet man auch viele spezielle Anwendungen wie klinische und epidemiologische Studien, räumliche Statistik, Lebensdauerprobleme, Human- und Populationsgenetik und Feldversuchswesen. Ein Verfahren beginnt mit der Beschreibung der Problemstellung, die aus den Teilen Planung (das bedeutet oft die Frage nach dem Versuchsumfang) und Auswertung besteht. Darauf folgt der aus den gleichen Teilen bestehende Lösungsweg sowie ein durchgerechnetes Beispiel. Der Verzicht auf Beweise macht die Verfahren leicht lesbar, die Beispiele erleichtern das Verständnis auch für Leser mit geringen Vorkenntnissen. Die Daten des Beispiels und das SAS-Programm können im jeweiligen Verfahren aufgerufen werden. Das Werk ist eine statistische Verfahrenssammlung zur Unterstützung experimenteller Forschungsarbeiten für höchste Ansprüche, insbesondere für klinische Studien, epidemiologische Studien, Geostatistik, Biometrie und landwirtschaftliches Versuchswesen. Das Werk wendet sich an Forscher aller Bereiche, die in ihrer Arbeit Versuche durchführen müssen.

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.