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Written by two specialists in statistics, the second edition of this text addresses the recommendations of the 1958 Report on Statistical Education for Engineers.
Disk contains: Portable MINITAB files.
"Written by two of the leading figures in statistics, this highly regarded volume thoroughly addresses the full range of required topics." provides early discussed fundamental concepts such as variability, graphical representation of data, and randomization and blocking in design of experiments. provides a thorough introduction to descriptive statistics, including the importance of understanding variability, representation of data, exploratory data analysis, and time-sequence plots. explores principles of probability, probability distributions, and sampling distribution theory. discusses regression, design of experiments and their analysis, including factorial and fractional factorial designs.
Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools Illustrations of how to use the outlined concepts in real-world situations Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.
This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Assuming no previous statistics education, this practical reference provides a comprehensive introduction and tutorial on the main statistical analysis topics, demonstrating their solution with the most common software package. Intended for anyone needing to apply statistical analysis to a large variety of science and enigineering problems, the book explains and shows how to use SPSS, MATLAB, STATISTICA and R for analysis such as data description, statistical inference, classification and regression, factor analysis, survival data and directional statistics. It concisely explains key concepts and methods, illustrated by practical examples using real data, and includes a CD-ROM with software tools and data sets used in the examples and exercises. Readers learn which software tools to apply and also gain insights into the comparative capabilities of the primary software packages.
The second edition of OCT and Imaging in Central Nervous System Diseases offers updated state-of-the-art advances using optical coherence tomography (OCT) regrading neuronal loss within the retina. Detailed information on the OCT imaging and interpretation is provided for the evaluation of disease progression in numerous neurodegenerative disorders and as a biological marker of neuroaxonal injury. Covering disorders like multiple sclerosis, Parkinson's disease, Alzheimer's disease, intracranial hypertension, Friedreich's ataxia, schizophrenia, hereditary optic neuropathies, glaucoma, and amblyopia, readers will given insights into effects on the retina and the and optic nerve. Individual chapters are also devoted to OCT technique, new OCT technology in neuro-ophthalmology, OCT and pharmacological treatment, and the use of OCT in animal models. Similar to the first edition, this book is an excellent and richly illustrated reference for diagnosis of many retinal diseases and monitoring of surgical and medical treatment. OCT allows to study vision from of the retina to the optic tracts. Retinal axons in the retinal nerve fiber layer (RNFL) are non-myelinated until they penetrate the lamina cribrosa. Hence, the RNFL is an ideal structure for visualization of any process of neurodegeneration, neuroprotection, or regeneration. By documenting the ability of OCT to provide key information on CNS diseases, this book illustrates convincingly that the eye is indeed the "window to the brain".