Few books on statistical data analysis in the natural sciences are written at a level that a non-statistician will easily understand. This is a book written in colloquial language, avoiding mathematical formulae as much as possible, trying to explain statistical methods using examples and graphics instead. To use the book efficiently, readers should have some computer experience. The book starts with the simplest of statistical concepts and carries readers forward to a deeper and more extensive understanding of the use of statistics in environmental sciences. The book concerns the application of statistical and other computer methods to the management, analysis and display of spatial data. These data are characterised by including locations (geographic coordinates), which leads to the necessity of using maps to display the data and the results of the statistical methods. Although the book uses examples from applied geochemistry, and a large geochemical survey in particular, the principles and ideas equally well apply to other natural sciences, e.g., environmental sciences, pedology, hydrology, geography, forestry, ecology, and health sciences/epidemiology. The book is unique because it supplies direct access to software solutions (based on R, the Open Source version of the S-language for statistics) for applied environmental statistics. For all graphics and tables presented in the book, the R-scripts are provided in the form of executable R-scripts. In addition, a graphical user interface for R, called DAS+R, was developed for convenient, fast and interactive data analysis. Statistical Data Analysis Explained: Applied Environmental Statistics with R provides, on an accompanying website, the software to undertake all the procedures discussed, and the data employed for their description in the book.
This book presents the statistical analysis of compositional data using the log-ratio approach. It includes a wide range of classical and robust statistical methods adapted for compositional data analysis, such as supervised and unsupervised methods like PCA, correlation analysis, classification and regression. In addition, it considers special data structures like high-dimensional compositions and compositional tables. The methodology introduced is also frequently compared to methods which ignore the specific nature of compositional data. It focuses on practical aspects of compositional data analysis rather than on detailed theoretical derivations, thus issues like graphical visualization and preprocessing (treatment of missing values, zeros, outliers and similar artifacts) form an important part of the book. Since it is primarily intended for researchers and students from applied fields like geochemistry, chemometrics, biology and natural sciences, economics, and social sciences, all the proposed methods are accompanied by worked-out examples in R using the package robCompositions.
Using formal descriptions, graphical illustrations, practical examples, and R software tools, Introduction to Multivariate Statistical Analysis in Chemometrics presents simple yet thorough explanations of the most important multivariate statistical methods for analyzing chemical data. It includes discussions of various statistical methods, such as
Applied Geochemistry: Advances in Mineral Exploration Techniques is a book targeting all levels of exploration geologists, geology students and geoscientists working in the mining industry. This reference book covers mineral exploration techniques from multiple dimensions, including the application of statistics - both principal component analysis and factor analysis - to multifractal modeling. The book explains these approaches step-by-step and gives their limitations. In addition to techniques and applications in mineral exploration, Applied Geochemistry describes mineral deposits and the theories underpinning their formation through worldwide case studies.
This is a textbook for graduate and final-year-undergraduate computer-science and electrical-engineering students interested in the hardware and software aspects of embedded and cyberphysical systems design. It is comprehensive and self-contained, covering everything from the basics to case-study implementation. Emphasis is placed on the physical nature of the problem domain and of the devices used. The reader is assumed to be familiar on a theoretical level with mathematical tools like ordinary differential equation and Fourier transforms. In this book these tools will be put to practical use. Engineering Embedded Systems begins by addressing basic material on signals and systems, before introducing to electronics. Treatment of digital electronics accentuating synchronous circuits and including high-speed effects proceeds to micro-controllers, digital signal processors and programmable logic. Peripheral units and decentralized networks are given due weight. The properties of analog circuits and devices like filters and data converters are covered to the extent desirable by a systems architect. The handling of individual elements concludes with power supplies including regulators and converters. The final section of the text is composed of four case studies: • electric-drive control, permanent magnet synchronous motors in particular; • lock-in amplification with measurement circuits for weight and torque, and moisture; • design of a simple continuous wave radar that can be operated to measure speed and distance; and • design of a Fourier transform infrared spectrometer for process applications. End-of-chapter exercises will assist the student to assimilate the tutorial material and these are supplemented by a downloadable solutions manual for instructors. The “pen-and-paper” problems are further augmented with laboratory activities. In addition to its student market, Engineering Embedded Systems will assist industrial practitioners working in systems architecture and the design of electronic measurement systems to keep up to date with developments in embedded systems through self study.
The field of brain imaging is developing at a rapid pace and has greatly advanced the areas of cognitive and clinical neuroscience. The availability of neuroimaging techniques, especially magnetic resonance imaging (MRI), functional MRI (fMRI), diffusion tensor imaging (DTI) and magnetoencephalography (MEG) and magnetic source imaging (MSI) has brought about breakthroughs in neuroscience. To obtain comprehensive information about the activity of the human brain, different analytical approaches should be complemented. Thus, in "intermodal multimodality" imaging, great efforts have been made to combine the highest spatial resolution (MRI, fMRI) with the best temporal resolution (MEG or EEG). "Intramodal multimodality" imaging combines various functional MRI techniques (e.g., fMRI, DTI, and/or morphometric/volumetric analysis). The multimodal approach is conceptually based on the combination of different noninvasive functional neuroimaging tools, their registration and cointegration. In particular, the combination of imaging applications that map different functional systems is useful, such as fMRI as a technique for the localization of cortical function and DTI as a technique for mapping of white matter fiber bundles or tracts. This booklet gives an insight into the wide field of multimodal imaging with respect to concepts, data acquisition, and postprocessing. Examples for intermodal and intramodal multimodality imaging are also demonstrated. Table of Contents: Introduction / Neurological Measurement Techniques and First Steps of Postprocessing / Coordinate Transformation / Examples for Multimodal Imaging / Clinical Aspects of Multimodal Imaging / References / Biography
This volume contains very carefully compiled material presenting bibliographic descriptions of approximately 3500 papers, with a computer-generated index on authors, subject headings, corporate addresses and journals. There are many on-line services available on fullerenes, but they serve mainly current-awareness functions; none of them is selectively complete and carefully indexed and none can replace a complete retrospective bibliography, which most researchers in the field would want to have on hand in their laboratories and offices.
Implantable Hearing Devices is written for ear, nose, and throat surgeons in training who must know about implantable hearing devices as they advance in otologic surgery. It is also a resource for otologic surgeons desiring to know more about the devices available. The technology is evolving rapidly along with the criteria for candidacy, and this text covers the entire spectrum of implantable hearing devices that are available, including but not limited to cochlear implants. Complex issues are presented in an easy to understand format by a host of internationally well-respected authors. Many practitioners have to refer to multiple resources for answers to their questions because the discipline is changing so rapidly. Implantable Hearing Devices is a clear, concise, but comprehensive book that offers answers to the universal problems that otologic surgeons face. Disclaimer: Please note that ancillary content (such as documents, audio, and video, etc.) may not be included as published in the original print version of this book.
This book presents the statistical analysis of compositional data using the log-ratio approach. It includes a wide range of classical and robust statistical methods adapted for compositional data analysis, such as supervised and unsupervised methods like PCA, correlation analysis, classification and regression. In addition, it considers special data structures like high-dimensional compositions and compositional tables. The methodology introduced is also frequently compared to methods which ignore the specific nature of compositional data. It focuses on practical aspects of compositional data analysis rather than on detailed theoretical derivations, thus issues like graphical visualization and preprocessing (treatment of missing values, zeros, outliers and similar artifacts) form an important part of the book. Since it is primarily intended for researchers and students from applied fields like geochemistry, chemometrics, biology and natural sciences, economics, and social sciences, all the proposed methods are accompanied by worked-out examples in R using the package robCompositions.
Few books on statistical data analysis in the natural sciences are written at a level that a non-statistician will easily understand. This is a book written in colloquial language, avoiding mathematical formulae as much as possible, trying to explain statistical methods using examples and graphics instead. To use the book efficiently, readers should have some computer experience. The book starts with the simplest of statistical concepts and carries readers forward to a deeper and more extensive understanding of the use of statistics in environmental sciences. The book concerns the application of statistical and other computer methods to the management, analysis and display of spatial data. These data are characterised by including locations (geographic coordinates), which leads to the necessity of using maps to display the data and the results of the statistical methods. Although the book uses examples from applied geochemistry, and a large geochemical survey in particular, the principles and ideas equally well apply to other natural sciences, e.g., environmental sciences, pedology, hydrology, geography, forestry, ecology, and health sciences/epidemiology. The book is unique because it supplies direct access to software solutions (based on R, the Open Source version of the S-language for statistics) for applied environmental statistics. For all graphics and tables presented in the book, the R-scripts are provided in the form of executable R-scripts. In addition, a graphical user interface for R, called DAS+R, was developed for convenient, fast and interactive data analysis. Statistical Data Analysis Explained: Applied Environmental Statistics with R provides, on an accompanying website, the software to undertake all the procedures discussed, and the data employed for their description in the book.
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