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 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.

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 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.

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.

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.