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In methodisch leicht fasslicher Weise werden Begriffe und Methoden der beurteilenden Statistik vorgestellt. Dabei wird gezeigt, dass die grundlegenden Verfahren der beurteilenden Statistik ebenfalls leicht mit Excel umsetzbar sind - ob es sich nun um die wichtigsten Parametertests, um Anpassungstests oder um einfache Varianzanalysen handelt.

Probabilities. --- Statistics . --- Application software. --- Applied mathematics. --- Engineering mathematics. --- Probability Theory and Stochastic Processes. --- Statistics, general. --- Computer Applications. --- Statistics and Computing/Statistics Programs. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Applications of Mathematics. --- Statistics.

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Microbes are very small and, as individuals, are capable of influencing a portion of the environment only slightly larger than their own body size, i.e., a few microns. However, their impact on the landscape is enormous, and ecosystem processes such as organic matter decomposition, denitrification, and metal oxidation/reduction are measured on scales of meters to kilometers. This volume highlights recent advances that have contributed to our understanding of spatial patterns and scale issues in microbial ecology, and brings together research conducted at a range of spatial scales (from µm to km) and in a variety of different types of environments. These topics are addressed in a quantitative manner, and a primer on statistical methods is included to aid the unfamiliar reader. In soil ecosystems, both bacteria and fungi are discussed, and the spatial patterns are interpreted in an ecological context that considers issues such as nutrient availability, vegetation distribution and growth patterns, and microbial colonization. In aquatic systems, focus is on the distribution of planktonic forms including phytoplankton and microzooplankton. The reader should gain insight on how to integrate information across spatial scales, which is necessary in order to understand and predict how these tiny organisms can have such a profound effect on landscape and ecosystem-level processes.

Microbial ecology. --- Microorganisms. --- Environmental microbiology --- Microorganisms --- Ecology --- Microbiology --- Germs --- Micro-organisms --- Microbes --- Microscopic organisms --- Organisms --- Statistics. --- Microbial Ecology. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Biogeosciences. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Statistics . --- Geobiology. --- Biology --- Earth sciences --- Biosphere

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Geostatistics is concerned with estimation and prediction problems for spatially continuous phenomena, using data obtained at a limited number of spatial locations. The name reflects its origins in mineral exploration, but the methods are now used in a wide range of settings including public health and the physical and environmental sciences. Model-based geostatistics refers to the application of general statistical principles of modeling and inference to geostatistical problems. This volume is the first book-length treatment of model-based geostatistics. The authors have written an expository text, emphasizing statistical methods and applications rather than the underlying mathematical theory. Analyses of datasets from a range of scientific contexts feature prominently, and simulations are used to illustrate theoretical results. Readers can reproduce most of the computational results in the book by using the authors' R-based software package, geoR, whose usage is illustrated in a computation section at the end of each chapter. The book assumes a working knowledge of classical and Bayesian methods of inference, linear models, and generalized linear models, but does not require previous exposure to spatial statistical models or methods. The authors have used the material in MSc-level statistics courses. Peter Diggle is Professor of Statistics at Lancaster University and Adjunct Professor of Biostatistics at Johns Hopkins University School of Public Health. Paulo Ribeiro is Senior Lecturer at Universidade Federal do Paraná.

Geology --- Statistical methods. --- Mathematical models. --- Geological statistics --- Geostatistics --- Geography. --- Mathematical statistics. --- Statistics. --- Earth Sciences, general. --- Statistical Theory and Methods. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Cosmography --- Earth sciences --- World history --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Earth sciences. --- Statistics . --- Geosciences --- Environmental sciences --- Physical sciences

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Die 6. Auflage basiert auf Programmversion 15. Die Autoren demonstrieren mit möglichst wenig Mathematik, detailliert und anschaulich anhand von Beispielen aus der Praxis die statistischen Methoden und deren Anwendungen. Der Anfänger findet für das Selbststudium einen sehr leichten Einstieg in das Programmsystem, für den erfahrenen SPSS-Anwender (auch früherer Versionen) ist das Buch ein hervorragendes Nachschlagewerk. Auf den zum Buch gehörenden Internetseiten sind alle Datendateien sowie weitere Informationen verfügbar. Aus Besprechungen zu den Vorauflagen: "Im Gegensatz zur Masse der SPSS-Bücher ist dieses Werk erfreulich verständlich und anwendungsorientiert geschrieben ... Viele Screenshots und gute Beispiele erleichtern sowohl die Anwendung von SPSS als auch das Verständnis der einzelnen Verfahren ... Sowohl für Praktiker als auch anwendungsorientierte Wissenschaftler und Studenten vorbehaltlos zu empfehlen. Im Doppelpack mit den Multivariaten von Backhaus et. al. in Breite und Tiefe nicht zu toppen." (amazon.de).

Computer software. --- Statistics . --- Economic theory. --- Social sciences. --- Mathematical Software. --- Statistics and Computing/Statistics Programs. --- Economic Theory/Quantitative Economics/Mathematical Methods. --- Methodology of the Social Sciences. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Statistics. --- Economics.

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No statistical model is "true" or "false," "right" or "wrong"; the models just have varying performance, which can be assessed. The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The intuitive and fundamental concepts of complexity, learnable information, and noise are formalized, which provides a firm information theoretic foundation for statistical modeling. Inspired by Kolmogorov's structure function in the algorithmic theory of complexity, this is accomplished by finding the shortest code length, called the stochastic complexity, with which the data can be encoded when advantage is taken of the models in a suggested class, which amounts to the MDL (Minimum Description Length) principle. The complexity, in turn, breaks up into the shortest code length for the optimal model in a set of models that can be optimally distinguished from the given data and the rest, which defines "noise" as the incompressible part in the data without useful information. Such a view of the modeling problem permits a unified treatment of any type of parameters, their number, and even their structure. Since only optimally distinguished models are worthy of testing, we get a logically sound and straightforward treatment of hypothesis testing, in which for the first time the confidence in the test result can be assessed. Although the prerequisites include only basic probability calculus and statistics, a moderate level of mathematical proficiency would be beneficial. The different and logically unassailable view of statistical modelling should provide excellent grounds for further research and suggest topics for graduate students in all fields of modern engineering, including and not restricted to signal and image processing, bioinformatics, pattern recognition, and machine learning to mention just a few. The author is an Honorary Doctor and Professor Emeritus of the Technical University of Tampere, Finland, a Fellow of Helsinki Institute for Information Technology, and visiting Professor in the Computer Learning Research Center of University of London, Holloway, England. He is also a Foreign Member of Finland's Academy of Science and Letters, an Associate Editor of IMA Journal of Mathematical Control and Information and of EURASIP Journal on Bioinformatics and Systems Biology. He is also a former Associate Editor of Source Coding of IEEE Transactions on Information Theory. The author is the recipient of the IEEE Information Theory Society's 1993 Richard W. Hamming medal for fundamental contributions to information theory, statistical inference, control theory, and the theory of complexity; the Information Theory Society's Golden Jubilee Award in 1998 for Technological Innovation for inventing Arithmetic Coding; and the 2006 Kolmogorov medal by University of London. He has also received an IBM Corporate Award for the MDL and PMDL Principles in 1991, and two best paper awards.

Statistics. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Distribution (Probability theory. --- Coding theory. --- Probability Theory and Stochastic Processes. --- Coding and Information Theory. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Data compression (Telecommunication) --- Digital electronics --- Information theory --- Machine theory --- Signal theory (Telecommunication) --- Computer programming --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Probabilities. --- Information theory. --- Statistics . --- Communication theory --- Communication --- Cybernetics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk