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Understanding spatial statistics requires tools from applied and mathematical statistics, linear model theory, regression, time series, and stochastic processes. It also requires a mindset that focuses on the unique characteristics of spatial data and the development of specialized analytical tools designed explicitly for spatial data analysis. Statistical Methods for Spatial Data Analysis answers the demand for a text that incorporates all of these factors by presenting a balanced exposition that explores both the theoretical foundations of the field of spatial statistics as well as practical methods for the analysis of spatial data. This book is a comprehensive and illustrative treatment of basic statistical theory and methods for spatial data analysis, employing a model-based and frequentist approach that emphasizes the spatial domain. It introduces essential tools and approaches including: measures of autocorrelation and their role in data analysis; the background and theoretical framework supporting random fields; the analysis of mapped spatial point patterns; estimation and modeling of the covariance function and semivariogram; a comprehensive treatment of spatial analysis in the spectral domain; and spatial prediction and kriging. The volume also delivers a thorough analysis of spatial regression, providing a detailed development of linear models with uncorrelated errors, linear models with spatially-correlated errors and generalized linear mixed models for spatial data. It succinctly discusses Bayesian hierarchical models and concludes with reviews on simulating random fields, non-stationary covariance, and spatio-temporal processes.Additional material on the CRC Press website supplements the content of this book. The site provides data sets used as examples in the text, software code that can be used to implement many of the principal methods described and illustrated, and updates to the text itself.

Complex-valued random signals are embedded in the very fabric of science and engineering, yet the usual assumptions made about their statistical behavior are often a poor representation of the underlying physics. This book deals with improper and noncircular complex signals, which do not conform to classical assumptions, and it demonstrates how correct treatment of these signals can have significant payoffs. The book begins with detailed coverage of the fundamental theory and presents a variety of tools and algorithms for dealing with improper and noncircular signals. It provides a comprehensive account of the main applications, covering detection, estimation, and signal analysis of stationary, nonstationary, and cyclostationary processes. Providing a systematic development from the origin of complex signals to their probabilistic description makes the theory accessible to newcomers. This book is ideal for graduate students and researchers working with complex data in a range of research areas from communications to oceanography.

The text is confined to the study of the equilibrium states of physical systems. It is well illustrated with numerous graphs, and a large number of problems are included.

The Seventh Edition of Statistical Quality Control provides a comprehensive treatment of the major aspects of using statistical methodology for quality control and improvement. Both traditional and modern methods are presented, including state-of-the-art techniques for statistical process monitoring and control and statistically designed experiments for process characterization, optimization, and process robustness studies. The seventh edition continues to focus on DMAIC (define, measure, analyze, improve, and control--the problem-solving strategy of six sigma) including a chapter on the implementation process. Additionally, the text includes new examples, exercises, problems, and techniques. Statistical Quality Control is best suited for students in engineering, statistics, business and management science or students in undergraduate courses.

As the field of transportation moves toward the "total quality management" paradigm, performance-based outcomes and quantitative measures have become increasingly important. Measuring performance in the field depends heavily on modeling trends and data, which in turn requires powerful, and flexible analytical tools. To date, however, transportation professionals have lacked a unified, rigorous guide to modeling the wide range of problems they encounter in the field. Statistical and Econometric Methods for Transportation Data describes the techniques most useful for modeling the many complex aspects of transportation, such as travel demand, safety, emissions, and the environment. Taking care not to overwhelm readers with statistical theory, the authors clearly and concisely present the relevant analytical methods in quantitative chapters built on transportation case studies. Mastering this material enables readers to:Formulate research hypothesesIdentify appropriate statistical and econometric modelsAvoid common pitfalls and misapplications of statistical methodsInterpret model results correctlyIdeal as both a textbook and reference, this book makes three unique contributions to transportation practice and education. First, it presents a host of analytical techniques-both common and sophisticated-used to model transportation phenomena. Second, it provides a wealth of examples and case studies, and third, it specifically targets present and future transportation professionals. It builds the foundation they need not only to apply analytical models but also to understand and interpret results published elsewhere.

* Includes a new chapter on logistic regression. * Discusses the design and analysis of random trials. * Explores the latest applications of sample size tables. * Contains a new section on binomial distribution.

Unread book in perfect condition.

This work is devoted to several problems of parametric (mainly) and nonparametric estimation through the observation of Poisson processes defined on general spaces. Poisson processes are quite popular in applied research and therefore they attract the attention of many statisticians. There are a lot of good books on point processes and many of them contain chapters devoted to statistical inference for general and partic­ ular models of processes. There are even chapters on statistical estimation problems for inhomogeneous Poisson processes in asymptotic statements. Nevertheless it seems that the asymptotic theory of estimation for nonlinear models of Poisson processes needs some development. Here nonlinear means the models of inhomogeneous Pois­ son processes with intensity function nonlinearly depending on unknown parameters. In such situations the estimators usually cannot be written in exact form and are given as solutions of some equations. However the models can be quite fruitful in en­ gineering problems and the existing computing algorithms are sufficiently powerful to calculate these estimators. Therefore the properties of estimators can be interesting too.

In the first part of this book, classical nonequilibrium statistical mechanics is developed. Starting from the Hamiltonian dynamics of the molecules, it leads through the irreversible kinetic equations to the level of fluid mechanics. For simple systems, all the transport coefficients are determined by the molecular properties. The second part of the book treats complex systems that require a more extensive use of statistical concepts. Such problems, which are at the forefront of research, include: continuous time random walks, non-Markovian diffusion processes, percolation and related critical phenomena, transport on fractal structures, transport and deterministic chaos. These ''strange transport processes'' differ significantly from the usual (diffusive) transport. Their inclusion in a general treatise on statistical mechanics is a special feature of this invaluable book.

Hardcover and dust jacket. Jacket spine sunned. Good binding and cover. Clean, unmarked pages. xii, 205 pages ; 23 cm. Examines the main features of the statistical movement in early Victorian Britain, especially in the 1830s and 1840s until its demise in 1852, in order to find out how it organized itself, to assess its achievements and to discover a distinctive philosophy, if it had one.

On-line and off-line quality control are the two methods used to discern a products reliability of quality. Though they are disparate techniques, both methods are used to achieve the same result. This introductory textbook integrates the two techniques to present a wide coverage of statistical methods of quality control. The text is compact, stressing the key ideas and concepts rather than trying to cover each method in complete depth. Statistical Aspects of Quality Control is an excellent starting point for a student interested in learning more about the field of statistical quality control. References and suggested readings are included at the end of each chapter.Presents statistical quality control in a compact fashion that stresses key ideas and conceptsUses the concept of Average Run Length to compare the different control charts, such as Shewhart, moving average, and cusumIntroduces the Taguchi approach to quality designIncludes information on acceptance samplingConcludes each chapter with final comments, references, and examples to illustrate the methods discussed

The description for this book, Statistical Physics and the Atomic Theory of Matter from Boyle and Newton to Landau and Onsager, will be forthcoming.

Used book that is in clean, average condition without any missing pages.

There's nothing random about it—this is the book on statistical analysis with Excel Stunned by statistics? Exhausted by Excel? Relax! Statistical Analysis with Excel For Dummies, 4th Edition shows you how to use the world's most popular spreadsheet program to crunch numbers and interpret statistics—even if you've never taken a statistics or advanced math course. In no time, you'll learn to use Excel to create and translate statistics in everyday life, understand common statistical terms, and improve your classroom or professional skills. Statistics has a reputation for being a challenging, math-intensive pursuit—but it doesn't have to make your palms sweat. Using a minimum of equations and assuming no prior knowledge of statistics or Excel, this hands-on guide cuts through the jargon and shows you how to make sense of formulas and functions, charts and PivotTables, samples and normal distributions, probabilities and related distributions, trends and correlations, and much more. Use Excel's tools to analyze and understand data Apply statistical analysis to predict trends and make decisions Interpret sales figures, gambling odds, and sports stats Develop a grading curve or medical correlations Forget the mumbo jumbo! This guide shows you that statistical analysis with Excel can be easy, fun, and useful!

The new edition of Lind’s Statistical Techniques in Business and Economics is a perennial market best seller due to its comprehensive coverage of statistical concepts and methods delivered in a student-friendly, step-by-step format. The text is non-threatening and presents concepts clearly and succinctly with a conversational writing style. All statistical concepts are illustrated with solved applied examples immediately upon introduction. Self reviews and exercises for each section, and review sections for groups of chapters also support the student learning steps. Modern computing applications (Excel, Minitab, and MegaStat) are introduced, but the text maintains a focus on presenting statistics concepts as applied in business as opposed to technology or programming methods. The thirteenth edition continues as a students’ text with increased emphasis on interpretation of data and results.

Dealing with statistical treatment of experimental data, this text covers topics such as errors, probability, the binomial distribution, the Poisson distribution, the Gauss distribution, method of least squares and standard deviation of the mean.

Very Good Condition - May show some limited signs of wear and may have a remainder mark. Pages and dust cover are intact and not marred by notes or highlighting.

The business, commercial and public-sector world has changed dramatically since John Oakland wrote the first edition of Statistical Process Control – a practical guide in the mid-eighties. Then people were rediscovering statistical methods of ‘quality control’ and the book responded to an often desperate need to find out about the techniques and use them on data. Pressure over time from organizations supplying directly to the consumer, typically in the automotive and high technology sectors, forced those in charge of the supplying production and service operations to think more about preventing problems than how to find and fix them. Subsequent editions retained the ‘took kit’ approach of the first but included some of the ‘philosophy’ behind the techniques and their use. The theme which runs throughout the 7th edition is still processes - that require understanding, have variation, must be properly controlled, have a capability, and need improvement - the five sections of this new edition. SPC never has been and never will be simply a ‘took kit’ and in this book the authors provide, not only the instructional guide for the tools, but communicate the management practices which have become so vital to success in organizations throughout the world. The book is supported by the authors' extensive and latest consulting work within thousands of organisations worldwide. Fully updated to include real-life case studies, new research based on client work from an array of industries, and integration with the latest computer methods and Minitab software, the book also retains its valued textbook quality through clear learning objectives and end of chapter discussion questions. It can still serve as a textbook for both student and practicing engineers, scientists, technologists, managers and for anyone wishing to understand or implement modern statistical process control techniques.

Eugene Charniak breaks new ground in artificial intelligence research by presenting statistical language processing from an artificial intelligence point of view in a text for researchers and scientists with a traditional computer science background. New, exacting empirical methods are needed to break the deadlock in such areas of artificial intelligence as robotics, knowledge representation, machine learning, machine translation, and natural language processing (NLP). It is time, Charniak observes, to switch paradigms. This text introduces statistical language processing techniques; word tagging, parsing with probabilistic context free grammars, grammar induction, syntactic disambiguation, semantic wordclasses, word-sense disambiguation; along with the underlying mathematics and chapter exercises. Charniak points out that as a method of attacking NLP problems, the statistical approach has several advantages. It is grounded in real text and therefore promises to produce usable results, and it offers an obvious way to approach learning: "one simply gathers statistics."

Develops a general model of statistical inference based on maximum likelihood for experimental situations in which multiple items are each measured by several different methods. The relationships among the different estimation methods are studied and recommendations are given on the methods to apply in given situations. Includes actual examples and computer listings to aid application.