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This collection contains invited papers by distinguished
statisticians to honour and acknowledge the contributions of
Professor Dr. Dr. Helge Toutenburg to Statistics on the occasion of
his sixty-?fth birthday. These papers present the most recent
developments in the area of the linear model and its related
topics. Helge Toutenburg is an established statistician and
currently a Professor in the Department of Statistics at the
University of Munich (Germany) and Guest Professor at the
University of Basel (Switzerland). He studied Mathematics in his
early years at Berlin and specialized in Statistics. Later he
completed his dissertation (Dr. rer. nat. ) in 1969 on optimal
prediction procedures at the University of Berlin and completed the
post-doctoral thesis in 1989 at the University of Dortmund on the
topic of mean squared error superiority. He taught at the
Universities of Berlin, Dortmund and Regensburg before joining the
University of Munich in 1991. He has various areas of interest in
which he has authored and co-authored over 130 research articles
and 17 books. He has made pioneering contributions in several areas
of statistics, including linear inference, linear models,
regression analysis, quality engineering, Taguchi methods, analysis
of variance, design of experiments, and statistics in medicine and
dentistry.
This book is the third revised and updated English edition of the
German textbook \Versuchsplanung und Modellwahl" by Helge
Toutenburg which was based on more than 15 years experience of
lectures on the course \- sign of Experiments" at the University of
Munich and interactions with the statisticians from industries and
other areas of applied sciences and en- neering. This is a type of
resource/ reference book which contains statistical methods used by
researchers in applied areas. Because of the diverse ex- ples
combined with software demonstrations it is also useful as a
textbook in more advanced courses, The applications of design of
experiments have seen a signi?cant growth in the last few decades
in di?erent areas like industries, pharmaceutical sciences, medical
sciences, engineering sciences etc. The second edition of this book
received appreciation from academicians, teachers, students and
applied statisticians. As a consequence, Springer-Verlag invited
Helge Toutenburg to revise it and he invited Shalabh for the third
edition of the book. In our experience with students, statisticians
from industries and - searchers from other ?elds of experimental
sciences, we realized the importance of several topics in the
design of experiments which will - crease the utility of this book.
Moreover we experienced that these topics are mostly explained only
theoretically in most of the available books.
Now in its second edition, this introductory statistics textbook
conveys the essential concepts and tools needed to develop and
nurture statistical thinking. It presents descriptive, inductive
and explorative statistical methods and guides the reader through
the process of quantitative data analysis. This revised and
extended edition features new chapters on logistic regression,
simple random sampling, including bootstrapping, and causal
inference. The text is primarily intended for undergraduate
students in disciplines such as business administration, the social
sciences, medicine, politics, and macroeconomics. It features a
wealth of examples, exercises and solutions with computer code in
the statistical programming language R, as well as supplementary
material that will enable the reader to quickly adapt the methods
to their own applications.
This introductory statistics textbook conveys the essential
concepts and tools needed to develop and nurture statistical
thinking. It presents descriptive, inductive and explorative
statistical methods and guides the reader through the process of
quantitative data analysis. In the experimental sciences and
interdisciplinary research, data analysis has become an integral
part of any scientific study. Issues such as judging the
credibility of data, analyzing the data, evaluating the reliability
of the obtained results and finally drawing the correct and
appropriate conclusions from the results are vital. The text is
primarily intended for undergraduate students in disciplines like
business administration, the social sciences, medicine, politics,
macroeconomics, etc. It features a wealth of examples, exercises
and solutions with computer code in the statistical programming
language R as well as supplementary material that will enable the
reader to quickly adapt all methods to their own applications.
This book is the third revised and updated English edition of the
German textbook \Versuchsplanung und Modellwahl" by Helge
Toutenburg which was based on more than 15 years experience of
lectures on the course \- sign of Experiments" at the University of
Munich and interactions with the statisticians from industries and
other areas of applied sciences and en- neering. This is a type of
resource/ reference book which contains statistical methods used by
researchers in applied areas. Because of the diverse ex- ples
combined with software demonstrations it is also useful as a
textbook in more advanced courses, The applications of design of
experiments have seen a signi?cant growth in the last few decades
in di?erent areas like industries, pharmaceutical sciences, medical
sciences, engineering sciences etc. The second edition of this book
received appreciation from academicians, teachers, students and
applied statisticians. As a consequence, Springer-Verlag invited
Helge Toutenburg to revise it and he invited Shalabh for the third
edition of the book. In our experience with students, statisticians
from industries and - searchers from other ?elds of experimental
sciences, we realized the importance of several topics in the
design of experiments which will - crease the utility of this book.
Moreover we experienced that these topics are mostly explained only
theoretically in most of the available books.
Revised and updated with the latest results, this Third Edition
explores the theory and applications of linear models. The authors
present a unified theory of inference from linear models and its
generalizations with minimal assumptions. They not only use least
squares theory, but also alternative methods of estimation and
testing based on convex loss functions and general estimating
equations. Highlights of coverage include sensitivity analysis and
model selection, an analysis of incomplete data, an analysis of
categorical data based on a unified presentation of generalized
linear models, and an extensive appendix on matrix theory.
This collection contains invited papers by distinguished
statisticians to honour and acknowledge the contributions of
Professor Dr. Dr. Helge Toutenburg to Statistics on the occasion of
his sixty-?fth birthday. These papers present the most recent
developments in the area of the linear model and its related
topics. Helge Toutenburg is an established statistician and
currently a Professor in the Department of Statistics at the
University of Munich (Germany) and Guest Professor at the
University of Basel (Switzerland). He studied Mathematics in his
early years at Berlin and specialized in Statistics. Later he
completed his dissertation (Dr. rer. nat. ) in 1969 on optimal
prediction procedures at the University of Berlin and completed the
post-doctoral thesis in 1989 at the University of Dortmund on the
topic of mean squared error superiority. He taught at the
Universities of Berlin, Dortmund and Regensburg before joining the
University of Munich in 1991. He has various areas of interest in
which he has authored and co-authored over 130 research articles
and 17 books. He has made pioneering contributions in several areas
of statistics, including linear inference, linear models,
regression analysis, quality engineering, Taguchi methods, analysis
of variance, design of experiments, and statistics in medicine and
dentistry.
Now in its second edition, this introductory statistics textbook
conveys the essential concepts and tools needed to develop and
nurture statistical thinking. It presents descriptive, inductive
and explorative statistical methods and guides the reader through
the process of quantitative data analysis. This revised and
extended edition features new chapters on logistic regression,
simple random sampling, including bootstrapping, and causal
inference. The text is primarily intended for undergraduate
students in disciplines such as business administration, the social
sciences, medicine, politics, and macroeconomics. It features a
wealth of examples, exercises and solutions with computer code in
the statistical programming language R, as well as supplementary
material that will enable the reader to quickly adapt the methods
to their own applications.
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Proteomics in Periodontology
Prakirti Chaudhary, Shalabh Mehrotra, Vartika Verma
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R1,678
Discovery Miles 16 780
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Ships in 10 - 15 working days
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If you are a web developer who wants to learn more about developing
applications in Flask and scale them with industry-standard
practices, this is the book for you. This book will also act as a
handy tool if you are aware of Flask's major extensions and want to
make the best use of them. It is assumed that you have knowledge of
Python and a basic understanding of Flask. If you are completely
new to Flask, reading the book from the first chapter and going
forward will help in getting acquainted with Flask as you go ahead.
Maturity of scientific theories has facilitated creation of
advanced technology of human-engineered complex systems. A major
challenge in these systems is online detection of behavioral
uncertainties due to gradual evolution of anomalies (i.e.,
deviations from the nominal condition). These anomalies may alter
the quasi-static behavior that causes performance degradation and
can eventually lead to catastrophic failures. Therefore, for safe
and reliable operation, it is essential to develop robust
analytical tools for online degradation monitoring and for
generating advanced warnings of emerging anomalies. Since it is
often infeasible to achieve the required modeling accuracy due to
the presence of i) high dimensionality, ii) non-stationarity
(possibly chaotic behavior), iii) nonlinearity, and iv) exogenous
disturbances, time series analysis of appropriate sensor data
provides one of the most powerful tools for degradation monitoring
of complex systems. This book presents a data-driven pattern
identification methodology, built upon multidisciplinary concepts
of Symbolic Dynamics, Automata Theory and Information Theory, with
diverse applications to complex electromechanical systems.
This introductory statistics textbook conveys the essential
concepts and tools needed to develop and nurture statistical
thinking. It presents descriptive, inductive and explorative
statistical methods and guides the reader through the process of
quantitative data analysis. In the experimental sciences and
interdisciplinary research, data analysis has become an integral
part of any scientific study. Issues such as judging the
credibility of data, analyzing the data, evaluating the reliability
of the obtained results and finally drawing the correct and
appropriate conclusions from the results are vital. The text is
primarily intended for undergraduate students in disciplines like
business administration, the social sciences, medicine, politics,
macroeconomics, etc. It features a wealth of examples, exercises
and solutions with computer code in the statistical programming
language R as well as supplementary material that will enable the
reader to quickly adapt all methods to their own applications.
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