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Dieses gut eingeführte Lehrbuch liegt inzwischen in der 8. Auflage vor. Enthalten sind die Aufgaben und Lösungen, die als separater Band erschienen waren. Behandelt werden die Grundbegriffe der Statistik, speziell elementare Stichprobentheorie, Parameterschätzung, Konfidenzintervalle, Testtheorie, Regression und Korrelation sowie die Varianzanalyse. Das Ziel des Autors ist es, die einzelnen Verfahren nicht nur zu beschreiben, sondern auch zu begründen, warum sie benutzt werden dürfen. Dabei wird die entsprechende Theorie elementar und möglichst anschaulich beschrieben. Manchmal wird auf ein Ergebnis aus der "Elementaren Einführung in die Wahrscheinlichkeitsrechnung" des Autors verwiesen. Die Begriffsbildung und die entsprechende Motivation werden zu Beginn eines Abschnitts in anschaulichen Beispielen vorgenommen. Weitere Beispiele und durchgerechnete Übungsaufgaben sollen zum besseren Verständnis beitragen.

Statistics . --- Probabilities. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Probability Theory and Stochastic Processes.

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Dieses Lehrbuch erklärt die Grundkonzepte der Datenanalyse sowie des statistischen Schätzens und Testens in kompakter Form. Die Darstellung zielt dabei auf eine klare Begriffsbildung und deren Begründung ab. So ist es ein Leichtes, sich das nötige Basiswissen über die Auswertung von Daten, den Umgang mit Wahrscheinlichkeiten und statistische Schlussweisen anzueignen. Zahlreiche Beispiele und Grafiken veranschaulichen die Begriffe. Das Buch richtet sich vornehmlich an Studierende der BWL und der technischen Disziplinen an Fachhochschulen und Universitäten. Es kann aber auch in allen anderen Gebieten zum Einsatz kommen, in denen statistische Methoden gebraucht werden. Die Autoren Tatjana Lange hat ihre ingenieurwissenschaftliche und mathematische Ausbildung an der TU Kiew erhalten und an der TU Ilmenau promoviert. Sie lehrte Regelungstechnik, Mathematik und Statistik an verschiedenen deutschen Hochschulen. Als Forscherin auf dem Gebiet der mathematischen Modellbildung war sie an der Ukrainischen Akademie der Wissenschaften in Kiew und an der Akademie der Wissenschaften Berlin tätig. Seit 1995 ist sie Professorin für Automatisierungstechnik an der Hochschule Merseburg. Karl Mosler hat Mathematik und Statistik an den Universitäten Heidelberg und München studiert sowie an der Technischen Universität München promoviert. Er lehrte Statistik und Operations Research, u.a. in Hamburg und Frankfurt/Oder. 1995 erhielt er die Professur für Statistik und Ökonometrie an der Universität zu Köln.

Statistics . --- Statistics for Business, Management, Economics, Finance, Insurance. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

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This undergraduate statistical quality assurance textbook clearly shows with real projects, cases and data sets how statistical quality control tools are used in practice.  Among the topics covered is a practical evaluation of measurement effectiveness for both continuous and discrete data.  Gauge Reproducibility and Repeatability methodology (including confidence intervals for Repeatability, Reproducibility and the Gauge Capability Ratio) is thoroughly developed. Process capability indices and corresponding confidence intervals are also explained.  In addition to process monitoring techniques, experimental design and analysis for process improvement are carefully presented. Factorial and Fractional Factorial arrangements of treatments and Response Surface methods are covered. Integrated throughout the book are rich sets of examples and problems that help readers gain a better understanding of where and how to apply statistical quality control tools.  These large and realistic problem sets in combination with the streamlined approach of the text and extensive supporting material facilitate reader understanding. Second Edition Improvements Extensive coverage of measurement quality evaluation (in addition to ANOVA Gauge R&R methodologies) New end-of-section exercises and revised-end-of-chapter exercises Two full sets of slides, one with audio to assist student preparation outside-of-class and another appropriate for professors’ lectures Substantial supporting material Supporting Material Seven R programs that support variables and attributes control chart construction and analyses, Gauge R&R methods, analyses of Fractional Factorial studies,  Propagation of Error analyses and Response Surface analyses Documentation for the R programs Excel data files associated with the end-of-chapter problem sets, most from real engineering settings.

Statistics. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Quality assurance --- Industrial engineering --- Statistical methods. --- Statistics .

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Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation,  and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.

Statistical science --- data mining --- statistisch onderzoek --- Data mining --- R (Computer program language) --- Statistics. --- R (Computer program language). --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

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These notes describe how to average and fit numerical data that have been obtained either by simulation or measurement. Following an introduction on how to estimate various average values, they discuss how to determine error bars on those estimates, and how to proceed for combinations of measured values. Techniques for fitting data to a given set of models will be described in the second part of these notes. This primer equips readers to properly derive the results covered, presenting the content in a style suitable for a physics audience. It also includes scripts in python, perl and gnuplot for performing a number of tasks in data analysis and fitting, thereby providing readers with a useful reference guide.

Physical measurements. --- Measurement   . --- Statistics . --- Physics. --- Measurement Science and Instrumentation. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Numerical and Computational Physics, Simulation.