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Das Buch bietet eine umfassende Einführung in die Statistik. Die
Autoren liefern eine integrierte Darstellung der deskriptiven
Statistik, der modernen Methoden der explorativen Datenanalyse und
der induktiven Statistik, einschließlich der Regressions- und
Varianzanalyse. Zahlreiche Beispiele mit realen Daten
veranschaulichen den Text. Geeignet als vorlesungsbegleitender
Text, aber auch zum Selbststudium für Studierende der Wirtschafts-
und Sozialwissenschaften sowie anderer Anwendungsdisziplinen und
als Einführung für Studenten der Statistik.
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.
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.
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.
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 textbook provides a comprehensive introduction to statistical
principles, concepts and methods that are essential in modern
statistics and data science. The topics covered include
likelihood-based inference, Bayesian statistics, regression,
statistical tests and the quantification of uncertainty. Moreover,
the book addresses statistical ideas that are useful in modern data
analytics, including bootstrapping, modeling of multivariate
distributions, missing data analysis, causality as well as
principles of experimental design. The textbook includes sufficient
material for a two-semester course and is intended for master's
students in data science, statistics and computer science with a
rudimentary grasp of probability theory. It will also be useful for
data science practitioners who want to strengthen their statistics
skills.
This textbook provides a comprehensive introduction to statistical
principles, concepts and methods that are essential in modern
statistics and data science. The topics covered include
likelihood-based inference, Bayesian statistics, regression,
statistical tests and the quantification of uncertainty. Moreover,
the book addresses statistical ideas that are useful in modern data
analytics, including bootstrapping, modeling of multivariate
distributions, missing data analysis, causality as well as
principles of experimental design. The textbook includes sufficient
material for a two-semester course and is intended for master's
students in data science, statistics and computer science with a
rudimentary grasp of probability theory. It will also be useful for
data science practitioners who want to strengthen their statistics
skills.
Das Buch bietet eine umfassende Einfuhrung in die Statistik. Die
Autoren liefern eine integrierte Darstellung der deskriptiven
Statistik, der modernen Methoden der explorativen Datenanalyse und
der induktiven Statistik, einschliesslich der Regressions- und
Varianzanalyse. Zahlreiche Beispiele mit realen Daten
veranschaulichen den Text. Geeignet als vorlesungsbegleitender
Text, aber auch zum Selbststudium fur Studierende der Wirtschafts-
und Sozialwissenschaften sowie anderer Anwendungsdisziplinen und
als Einfuhrung fur Studenten der Statistik.
Das Fach Statistik ist in vielen Studiengangen Teil des
Grundstudiums. Wegen des mathematisch begrundeten Vorgehens haben
Studenten haufig Verstandnisprobleme. Das Arbeitsbuch ist eine
effektive Lernhilfe fur die Vorlesungen Statistik I und II und
erganzt die zwei Lehrbucher Deskriptive Statistik und Induktive
Statistik. Jedes Kapitel besteht aus einem Lehrteil, der die
wichtigsten Zusammenhange anhand klar strukturierter Beispiele
erlautert, sowie einem kommentierten Aufgabenteil. Datensatze fur
zusatzliche Ubungen mit SPSS als Download erhaltlich."
Statistische Verfahren werden in der Medizin und in allen
Naturwissenschaften, in der Wirtschaft, in der Technik und
zunehmend auch in den Sozial- und Geisteswissenschaften eingesetzt.
Die Statistik gilt trotzdem als schwierig. Um diese Hemmschwelle zu
uberwinden, geben die Autoren in dem vorliegenden Buch eine
anwendungsorientierte Einfuhrung in die Methoden der induktiven
Statistik und Datenanalyse. Sie beschreiben anhand praxisnaher
Beispiele die Ideen und Werkzeuge des modernen statistischen
Datenmanagements. Der Leser kann mittels der vielen Ubungsaufgaben
sein Wissen vertiefen, wobei die Musterlosungen ihm zeigen, wie
eine Ubung gelost werden konnte. Sowohl die Statistik-Software SPSS
als auch als Neuerung - die Programmiersprache R kommen in diesem
Buch zum Einsatz. Das Buch beinhaltet ferner eine Einfuhrung zur
Problematik fehlender Daten. Diese Erweiterung ist einmalig fur ein
deutschsprachiges Lehrbuch der Statistik."
Statistische Verfahren werden sowohl in der Wirtschaft als auch
in den Natur- und Sozialwissenschaften eingesetzt. Die Statistik
gilt trotzdem als schwierig. Um diese Hemmschwelle zu uberwinden,
geben die Autoren eine didaktisch ausgefeilte, anwendungsbezogene
Einfuhrung in die Methoden der deskriptiven Statistik und
Datenanalyse. Anhand praxisnaher Beispiele werden die Ideen des
Datenmanagements und der Datenauswertung unter Einsatz von SPSS und
R beschrieben. Viele Ubungsaufgaben (mit Losungen) unterstutzen das
(Selbst-) Studium der Leser. Das Buch deckt den Stoff Statistik I
an deutschsprachigen Universitaten vollstandig ab. Neu in dieser
Auflage ist eine Einfuhrung in die logistische Regression, deren
Konzept auch anhand der statistischen Software SPSS und R erlautert
wird. Des Weiteren wurden viele Beispiele und Ubungsaufgaben
thematisch uberarbeitet."
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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