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An Introduction to Multivariate Statistical Analysis
The multivariate normal distribution; Estimation of the mean vector and the covariance matrix; The distributions and uses of sample correlation coefficients; The generalized T2 statistic; Classification of observations; The distribution of the sample covariance matrix and the sample generalized variance; Testing the general linear hypothesis; analysis of variance; Testing independence of sets of variates; Testing hypotheses of equality of covariance matrices and equality of mean vectors and covariance matrices; Principal components; Canonical correlation and canonical variables; The distribution of certain characteristic roots and vectors that do not depend on parameters; A review of some other work in multivariate analysis.
Big Data
This Springer Brief provides a comprehensive overview of the background and recent developments of big data. The value chain of big data is divided into four phases: data generation, data acquisition, data storage and data analysis. For each phase, the book introduces the general background, discusses technical challenges and reviews the latest advances. Technologies under discussion include cloud computing, Internet of Things, data centers, Hadoop and more. The authors also explore several representative applications of big data such as enterprise management, online social networks, healthcare and medical applications, collective intelligence and smart grids. This book concludes with a thoughtful discussion of possible research directions and development trends in the field. Big Data: Related Technologies, Challenges and Future Prospects is a concise yet thorough examination of this exciting area. It is designed for researchers and professionals interested in big data or related research. Advanced-level students in computer science and electrical engineering will also find this book useful.
Applied Multivariate Statistics with R
Now in its second edition, this book brings multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source shareware program R, Dr. Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays; linear algebra; univariate, bivariate and multivariate normal distributions; factor methods; linear regression; discrimination and classification; clustering; time series models; and additional methods. He uses practical examples from diverse disciplines, to welcome readers from a variety of academic specialties. Each chapter includes exercises, real data sets, and R implementations. The book avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience with R is not necessary. New to this edition are chapters devoted to longitudinal studies and the clustering of large data. It is an excellent resource for students of multivariate statistics, as well as practitioners in the health and life sciences who are looking to integrate statistics into their work.
Protecting Chips Against Hold Time Violations Due to Variability
With the development of Very-Deep Sub-Micron technologies, process variability is becoming increasingly important and is a very important issue in the design of complex circuits. Process variability is the statistical variation of process parameters, meaning that these parameters do not have always the same value, but become a random variable, with a given mean value and standard deviation. This effect can lead to several issues in digital circuit design. The logical consequence of this parameter variation is that circuit characteristics, as delay and power, also become random variables. Because of the delay variability, not all circuits will now have the same performance, but some will be f...
Industrial Data Analytics for Diagnosis and Prognosis
Discover data analytics methodologies for the diagnosis and prognosis of industrial systems under a unified random effects model In Industrial Data Analytics for Diagnosis and Prognosis - A Random Effects Modelling Approach, distinguished engineers Shiyu Zhou and Yong Chen deliver a rigorous and practical introduction to the random effects modeling approach for industrial system diagnosis and prognosis. In the book’s two parts, general statistical concepts and useful theory are described and explained, as are industrial diagnosis and prognosis methods. The accomplished authors describe and model fixed effects, random effects, and variation in univariate and multivariate datasets and cover ...
The Concise Encyclopedia of Statistics
The Concise Encyclopedia of Statistics presents the essential information about statistical tests, concepts, and analytical methods in language that is accessible to practitioners and students of the vast community using statistics in medicine, engineering, physical science, life science, social science, and business/economics. The reference is alphabetically arranged to provide quick access to the fundamental tools of statistical methodology and biographies of famous statisticians. The more than 500 entries include definitions, history, mathematical details, limitations, examples, references, and further readings. All entries include cross-references as well as the key citations. The back matter includes a timeline of statistical inventions. This reference will be an enduring resource for locating convenient overviews about this essential field of study.
The New Statistical Analysis of Data
A non-calculus based introduction for students studying statistics, business, engineering, health sciences, social sciences, and education. It presents a thorough coverage of statistical techniques and includes numerous examples largely drawn from actual research studies. Little mathematical background is required and explanations of important concepts are based on providing intuition using illustrative figures and numerical examples. The first part shows how statistical methods are used in diverse fields in answering important questions, while part two covers descriptive statistics and considers the organisation and summarisation of data. Parts three to five cover probability, statistical inference, and more advanced statistical techniques.
