multivariate process capability assessment for non normal quality data

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David Fischer Pesticide Risk Assessment for Pollinators David Fischer Pesticide Risk Assessment for Pollinators
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David Fischer Pesticide Risk Assessment for Pollinators


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10044.45 руб.

Pollinators play a vital role in ecosystem health and are essential to ensuring food security. With declines in both managed and wild pollinator populations in recent years, scientists and regulators have sought answers to this problem and have explored implementing steps to protect pollinator populations now and for the future. Pesticide Risk Assessment for Pollinators focuses on the role pesticides play in impacting bee populations and looks to develop a risk assessment process, along with the data to inform that process, to better assess the potential risks that can accompany the use of pesticide products. Pesticide Risk Assessment for Pollinators opens with two chapters that provide a biological background of both Apis and non-Apis species of pollinators. Chapters then present an overview of the general regulatory risk assessment process and decision-making processes. The book then discusses the core elements of a risk assessment, including exposure estimation, laboratory testing, and field testing. The book concludes with chapters on statistical and modeling tools, and proposed additional research that may be useful in developing the ability to assess the impacts of pesticide use on pollinator populations. Summarizing the current state of the science surrounding risk assessment for Apis and non-Apis species, Pesticide Risk Assessment for Pollinators is a timely work that will be of great use to the environmental science and agricultural research communities. Assesses pesticide risk to native and managed pollinators Summarizes the state of the science in toxicity testing and risk assessment Provides valuable biological overviews of both Apis and non-Apis pollinators Develops a plausible overall risk assessment framework for regulatory decision making Looks towards a globally harmonized approach for pollinator toxicity and risk assessment

Blaise Amendolace Essentials of MCMI-IV Assessment Blaise Amendolace Essentials of MCMI-IV Assessment
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Blaise Amendolace Essentials of MCMI-IV Assessment


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3749.42 руб.

Quickly acquire the knowledge and skills you need to administer, score, and interpret the MCMI ®-IV Essentials of MCMI ®-IV Assessment is the definitive source of up-to-date, practical information for clinicians and students using the MCMI®-IV inventory. Step-by-step guidelines walk you through the process of administering the assessment, with a profile and demonstration of the clinical process from administration to treatment. Expert discussion helps inform higher-quality therapeutic interventions. The link between assessment and intervention is emphasized throughout, as well as coverage of relevant populations and clinical applications, to provide a well-rounded understanding while illuminating the uses of the MCMI ®-IV. This book provides instruction and clarification from the foremost experts to help you achieve better outcomes for your clients. Follow step-by-step guidelines for administering the MCMI ®-IV Recognize the connection between data and intervention Improve quality and accuracy of therapeutic applications Gain a more practical understanding of the MCMI ®-IV assessment process The MCMI ®-IV assesses a wide range of information related to a client's personality, emotional adjustment, test-taking approach, and other critical information. Interpretation and reporting serve as a basis from which therapeutic interventions are designed, so quality and accuracy is of utmost importance every step of the way. Essentials of MCMI ®-IV Assessment is the most authoritative, up-to-date resource in the field, and a must-have reference for anyone who uses the test.

Mohammad Arashi Statistical Inference for Models with Multivariate t-Distributed Errors Mohammad Arashi Statistical Inference for Models with Multivariate t-Distributed Errors
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Mohammad Arashi Statistical Inference for Models with Multivariate t-Distributed Errors


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8548.95 руб.

This book summarizes the results of various models under normal theory with a brief review of the literature. Statistical Inference for Models with Multivariate t-Distributed Errors: Includes a wide array of applications for the analysis of multivariate observations Emphasizes the development of linear statistical models with applications to engineering, the physical sciences, and mathematics Contains an up-to-date bibliography featuring the latest trends and advances in the field to provide a collective source for research on the topic Addresses linear regression models with non-normal errors with practical real-world examples Uniquely addresses regression models in Student's t-distributed errors and t-models Supplemented with an Instructor's Solutions Manual, which is available via written request by the Publisher

Suzanne Hendrix B. Multivariate Analysis for the Biobehavioral and Social Sciences. A Graphical Approach Suzanne Hendrix B. Multivariate Analysis for the Biobehavioral and Social Sciences. A Graphical Approach
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Suzanne Hendrix B. Multivariate Analysis for the Biobehavioral and Social Sciences. A Graphical Approach


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8699.07 руб.

An insightful guide to understanding and visualizing multivariate statistics using SAS®, STATA®, and SPSS® Multivariate Analysis for the Biobehavioral and Social Sciences: A Graphical Approach outlines the essential multivariate methods for understanding data in the social and biobehavioral sciences. Using real-world data and the latest software applications, the book addresses the topic in a comprehensible and hands-on manner, making complex mathematical concepts accessible to readers. The authors promote the importance of clear, well-designed graphics in the scientific process, with visual representations accompanying the presented classical multivariate statistical methods . The book begins with a preparatory review of univariate statistical methods recast in matrix notation, followed by an accessible introduction to matrix algebra. Subsequent chapters explore fundamental multivariate methods and related key concepts, including: Factor analysis and related methods Multivariate graphics Canonical correlation Hotelling's T-squared Multivariate analysis of variance (MANOVA) Multiple regression and the general linear model (GLM) Each topic is introduced with a research-publication case study that demonstrates its real-world value. Next, the question «how do you do that?» is addressed with a complete, yet simplified, demonstration of the mathematics and concepts of the method. Finally, the authors show how the analysis of the data is performed using Stata®, SAS®, and SPSS®. The discussed approaches are also applicable to a wide variety of modern extensions of multivariate methods as well as modern univariate regression methods. Chapters conclude with conceptual questions about the meaning of each method; computational questions that test the reader's ability to carry out the procedures on simple datasets; and data analysis questions for the use of the discussed software packages. Multivariate Analysis for the Biobehavioral and Social Sciences is an excellent book for behavioral, health, and social science courses on multivariate statistics at the graduate level. The book also serves as a valuable reference for professionals and researchers in the social, behavioral, and health sciences who would like to learn more about multivariate analysis and its relevant applications.

Daniel Larose T. Discovering Knowledge in Data. An Introduction to Data Mining Daniel Larose T. Discovering Knowledge in Data. An Introduction to Data Mining
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Daniel Larose T. Discovering Knowledge in Data. An Introduction to Data Mining


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7230.68 руб.

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website for university instructors who adopt the book

Rajesh Jugulum Competing with High Quality Data. Concepts, Tools, and Techniques for Building a Successful Approach to Data Quality Rajesh Jugulum Competing with High Quality Data. Concepts, Tools, and Techniques for Building a Successful Approach to Data Quality
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Rajesh Jugulum Competing with High Quality Data. Concepts, Tools, and Techniques for Building a Successful Approach to Data Quality


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7123.89 руб.

Create a competitive advantage with data quality Data is rapidly becoming the powerhouse of industry, but low-quality data can actually put a company at a disadvantage. To be used effectively, data must accurately reflect the real-world scenario it represents, and it must be in a form that is usable and accessible. Quality data involves asking the right questions, targeting the correct parameters, and having an effective internal management, organization, and access system. It must be relevant, complete, and correct, while falling in line with pervasive regulatory oversight programs. Competing with High Quality Data: Concepts, Tools and Techniques for Building a Successful Approach to Data Quality takes a holistic approach to improving data quality, from collection to usage. Author Rajesh Jugulum is globally-recognized as a major voice in the data quality arena, with high-level backgrounds in international corporate finance. In the book, Jugulum provides a roadmap to data quality innovation, covering topics such as: The four-phase approach to data quality control Methodology that produces data sets for different aspects of a business Streamlined data quality assessment and issue resolution A structured, systematic, disciplined approach to effective data gathering The book also contains real-world case studies to illustrate how companies across a broad range of sectors have employed data quality systems, whether or not they succeeded, and what lessons were learned. High-quality data increases value throughout the information supply chain, and the benefits extend to the client, employee, and shareholder. Competing with High Quality Data: Concepts, Tools and Techniques for Building a Successful Approach to Data Quality provides the information and guidance necessary to formulate and activate an effective data quality plan today.

Nancy McMunn D. A Teacher's Guide to Classroom Assessment. Understanding and Using Assessment to Improve Student Learning Nancy McMunn D. A Teacher's Guide to Classroom Assessment. Understanding and Using Assessment to Improve Student Learning
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Nancy McMunn D. A Teacher's Guide to Classroom Assessment. Understanding and Using Assessment to Improve Student Learning


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2563.22 руб.

A Teacher’s Guide to Classroom Assessment is a comprehensive guide that shows step-by-step how to effectively integrate assessment into the classroom. Written for both new and seasoned teachers, this important book offers a practical aid for developing assessment skills and strategies, building assessment literacy, and ultimately improving student learning. Based on extensive research, this book is filled with illustrative, down-to-earth examples of how classroom assessment works in classrooms where assessment drives the instruction. The authors present the Classroom Assessment Cycle—Clarifying learning targets, Collecting assessment evidence, Analyzing assessment data, and Modifying instruction based upon assessment data—that demonstrates how one assessment action must flow into the next to be effective. Each chapter details the kinds of assessment evidence that are the most useful for determining student achievement and provides instruction in the analysis of assessment data.

A. Wright Jordan Conducting Psychological Assessment. A Guide for Practitioners A. Wright Jordan Conducting Psychological Assessment. A Guide for Practitioners
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A. Wright Jordan Conducting Psychological Assessment. A Guide for Practitioners


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5056.42 руб.

A Valuable Guide to the Entire Process of Psychological Assessment Carefully working through all the phases of assessment, including integrating, conceptualizing, test selection, administering, scoring, and report writing, Conducting Psychological Assessment provides clinicians with a step-by-step methodology for conducting skilled individual assessments, from beginning to end. Unlike most guides to assessment, this book addresses the critical steps that follow administration, scoring, and interpretation—namely the integration of the data into a fully conceptualized report. Rich with case studies that illustrate every major point, this text provides a coherent structure for the entire process, taking into account the imperfection of both clinical intuition and specific psychological tests. Conducting Psychological Assessment presents practitioners with an accessible framework to help make the process of psychological assessment quicker, easier, and more efficient. It offers a model designed to ensure that assessors provide ethical and competent services and make useful contributions to the lives of the individuals they assess.

Banta Trudy W. Assessment Clear and Simple. A Practical Guide for Institutions, Departments, and General Education Banta Trudy W. Assessment Clear and Simple. A Practical Guide for Institutions, Departments, and General Education
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Banta Trudy W. Assessment Clear and Simple. A Practical Guide for Institutions, Departments, and General Education


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2722.69 руб.

The first edition of Assessment Clear and Simple quickly became the essential go-to guide for anyone who participates in the assessment process in higher education. With the increased pressure to perform assessment to demonstrate accountability, Assessment Clear and Simple is needed more than ever. This second edition of the classic resource offers a concise, step-by-step guide that helps make assessment simple, cost-efficient, and useful to an institution. It contains effective strategies for meeting the requirements of accreditation agencies, legislatures, review boards, and others, while emphasizing and showing how to move from data to actions that improve student learning. This thoroughly revised and updated edition includes many new or expanded features, including: Illustrative examples drawn from the author's experience consulting with more than 350 institutions A basic, no-frills assessment plan for departments and for general education Tips on how to integrate portfolios and e-portfolios into the assessment process Suggestions for using rubrics and alternatives to rubrics, including doing assessment for multidisciplinary work Clear instructions on how to construct a coherent institution-wide assessment system and explain it to accreditors Ideas for assigning responsibility for general education assessment Strategies for gathering information about departmental assessment while keeping the departmental workload manageable Information on how to manage assessment in times of budgetary cutbacks Praise for the Second Edition of Assessment Clear and Simple «Walvoord's approach to assessment is wonderfully straightforward; it is also effective in facilitating faculty engagement in assessment. We've applied a number of her methods to our campus assessment efforts with success. This book makes assessment both manageable and useful in improving and enhancing student learning.»—Martha L. A. Stassen, director of assessment, University of Massachusetts, Amherst, and president, New England Educational Assessment Network (NEEAN) «Walvoord's work clearly presents the basics for getting started in assessment of student learning while honestly addressing the complexities of assessment when driven by faculty passion for student learning. This book is a valuable resource for the novice as well as the developing experts who are leading their institutions in academic assessment.»—Bobbi Allen, faculty assessment director, Delta College

William Wei W.S. Multivariate Time Series Analysis and Applications William Wei W.S. Multivariate Time Series Analysis and Applications
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William Wei W.S. Multivariate Time Series Analysis and Applications


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9334.93 руб.

An essential guide on high dimensional multivariate time series including all the latest topics from one of the leading experts in the field Following the highly successful and much lauded book, Time Series Analysis—Univariate and Multivariate Methods, this new work by William W.S. Wei focuses on high dimensional multivariate time series, and is illustrated with numerous high dimensional empirical time series. Beginning with the fundamentalconcepts and issues of multivariate time series analysis,this book covers many topics that are not found in general multivariate time series books. Some of these are repeated measurements, space-time series modelling, and dimension reduction. The book also looks at vector time series models, multivariate time series regression models, and principle component analysis of multivariate time series. Additionally, it provides readers with information on factor analysis of multivariate time series, multivariate GARCH models, and multivariate spectral analysis of time series. With the development of computers and the internet, we have increased potential for data exploration. In the next few years, dimension will become a more serious problem. Multivariate Time Series Analysis and its Applications provides some initial solutions, which may encourage the development of related software needed for the high dimensional multivariate time series analysis. Written by bestselling author and leading expert in the field Covers topics not yet explored in current multivariate books Features classroom tested material Written specifically for time series courses Multivariate Time Series Analysis and its Applications is designed for an advanced time series analysis course. It is a must-have for anyone studying time series analysis and is also relevant for students in economics, biostatistics, and engineering.

David Scott W. Multivariate Density Estimation. Theory, Practice, and Visualization David Scott W. Multivariate Density Estimation. Theory, Practice, and Visualization
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David Scott W. Multivariate Density Estimation. Theory, Practice, and Visualization


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8623.66 руб.

Clarifies modern data analysis through nonparametric density estimation for a complete working knowledge of the theory and methods Featuring a thoroughly revised presentation, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition maintains an intuitive approach to the underlying methodology and supporting theory of density estimation. Including new material and updated research in each chapter, the Second Edition presents additional clarification of theoretical opportunities, new algorithms, and up-to-date coverage of the unique challenges presented in the field of data analysis. The new edition focuses on the various density estimation techniques and methods that can be used in the field of big data. Defining optimal nonparametric estimators, the Second Edition demonstrates the density estimation tools to use when dealing with various multivariate structures in univariate, bivariate, trivariate, and quadrivariate data analysis. Continuing to illustrate the major concepts in the context of the classical histogram, Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition also features: Over 150 updated figures to clarify theoretical results and to show analyses of real data sets An updated presentation of graphic visualization using computer software such as R A clear discussion of selections of important research during the past decade, including mixture estimation, robust parametric modeling algorithms, and clustering More than 130 problems to help readers reinforce the main concepts and ideas presented Boxed theorems and results allowing easy identification of crucial ideas Figures in color in the digital versions of the book A website with related data sets Multivariate Density Estimation: Theory, Practice, and Visualization, Second Edition is an ideal reference for theoretical and applied statisticians, practicing engineers, as well as readers interested in the theoretical aspects of nonparametric estimation and the application of these methods to multivariate data. The Second Edition is also useful as a textbook for introductory courses in kernel statistics, smoothing, advanced computational statistics, and general forms of statistical distributions.

Jillian Kinzie Assessment Essentials. Planning, Implementing, and Improving Assessment in Higher Education Jillian Kinzie Assessment Essentials. Planning, Implementing, and Improving Assessment in Higher Education
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Jillian Kinzie Assessment Essentials. Planning, Implementing, and Improving Assessment in Higher Education


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3749.42 руб.

A comprehensive expansion to the essential higher education assessment text This second edition of Assessment Essentials updates the bestselling first edition, the go-to resource on outcomes assessment in higher education. In this thoroughly revised edition, you will find, in a familiar framework, nearly all new material, examples from more than 100 campuses, and indispensable descriptions of direct and indirect assessment methods that have helped to educate faculty, staff, and students about assessment. Outcomes assessment is of increasing importance in higher education, especially as new technologies and policy proposals spotlight performance-based success measures. Leading authorities Trudy Banta and Catherine Palomba draw on research, standards, and best practices to address the timeless and timeliest issues in higher education accountability. New topics include: Using electronic portfolios in assessment Rubrics and course-embedded assessment Assessment in student affairs Assessing institutional effectiveness As always, the step-by-step approach of Assessment Essentials will guide you through the process of developing an assessment program, from the research and planning phase to implementation and beyond, with more than 100 examples along the way. Assessment data are increasingly being used to guide everything from funding to hiring to curriculum decisions, and all faculty and staff will need to know how to use them effectively. Perfect for anyone new to the assessment process, as well as for the growing number of assessment professionals, this expanded edition of Assessment Essentials will be an essential resource on every college campus.

Yasunori Fujikoshi Multivariate Statistics. High-Dimensional and Large-Sample Approximations Yasunori Fujikoshi Multivariate Statistics. High-Dimensional and Large-Sample Approximations
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Yasunori Fujikoshi Multivariate Statistics. High-Dimensional and Large-Sample Approximations


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11172.83 руб.

A comprehensive examination of high-dimensional analysis of multivariate methods and their real-world applications Multivariate Statistics: High-Dimensional and Large-Sample Approximations is the first book of its kind to explore how classical multivariate methods can be revised and used in place of conventional statistical tools. Written by prominent researchers in the field, the book focuses on high-dimensional and large-scale approximations and details the many basic multivariate methods used to achieve high levels of accuracy. The authors begin with a fundamental presentation of the basic tools and exact distributional results of multivariate statistics, and, in addition, the derivations of most distributional results are provided. Statistical methods for high-dimensional data, such as curve data, spectra, images, and DNA microarrays, are discussed. Bootstrap approximations from a methodological point of view, theoretical accuracies in MANOVA tests, and model selection criteria are also presented. Subsequent chapters feature additional topical coverage including: High-dimensional approximations of various statistics High-dimensional statistical methods Approximations with computable error bound Selection of variables based on model selection approach Statistics with error bounds and their appearance in discriminant analysis, growth curve models, generalized linear models, profile analysis, and multiple comparison Each chapter provides real-world applications and thorough analyses of the real data. In addition, approximation formulas found throughout the book are a useful tool for both practical and theoretical statisticians, and basic results on exact distributions in multivariate analysis are included in a comprehensive, yet accessible, format. Multivariate Statistics is an excellent book for courses on probability theory in statistics at the graduate level. It is also an essential reference for both practical and theoretical statisticians who are interested in multivariate analysis and who would benefit from learning the applications of analytical probabilistic methods in statistics.

David Machin Quality of Life. The Assessment, Analysis and Reporting of Patient-reported Outcomes David Machin Quality of Life. The Assessment, Analysis and Reporting of Patient-reported Outcomes
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David Machin Quality of Life. The Assessment, Analysis and Reporting of Patient-reported Outcomes


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7683.81 руб.

The assessment of patient reported outcomes and health-related quality of life continue to be rapidly evolving areas of research and this new edition reflects the development within the field from an emerging subject to one that is an essential part of the assessment of clinical trials and other clinical studies. The analysis and interpretation of quality-of-life assessments relies on a variety of psychometric and statistical methods which are explained in this book in a non-technical way. The result is a practical guide that covers a wide range of methods and emphasizes the use of simple techniques that are illustrated with numerous examples, with extensive chapters covering qualitative and quantitative methods and the impact of guidelines. The material in this new third edition reflects current teaching methods and content widened to address continuing developments in item response theory, computer adaptive testing, analyses with missing data, analysis of ordinal data, systematic reviews and meta-analysis. This book is aimed at everyone involved in quality-of-life research and is applicable to medical and non-medical, statistical and non-statistical readers. It is of particular relevance for clinical and biomedical researchers within both the pharmaceutical industry and clinical practice.

Michael Sherman Spatial Statistics and Spatio-Temporal Data. Covariance Functions and Directional Properties Michael Sherman Spatial Statistics and Spatio-Temporal Data. Covariance Functions and Directional Properties
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Michael Sherman Spatial Statistics and Spatio-Temporal Data. Covariance Functions and Directional Properties


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9019.88 руб.

In the spatial or spatio-temporal context, specifying the correct covariance function is fundamental to obtain efficient predictions, and to understand the underlying physical process of interest. This book focuses on covariance and variogram functions, their role in prediction, and appropriate choice of these functions in applications. Both recent and more established methods are illustrated to assess many common assumptions on these functions, such as, isotropy, separability, symmetry, and intrinsic correlation. After an extensive introduction to spatial methodology, the book details the effects of common covariance assumptions and addresses methods to assess the appropriateness of such assumptions for various data structures. Key features: An extensive introduction to spatial methodology including a survey of spatial covariance functions and their use in spatial prediction (kriging) is given. Explores methodology for assessing the appropriateness of assumptions on covariance functions in the spatial, spatio-temporal, multivariate spatial, and point pattern settings. Provides illustrations of all methods based on data and simulation experiments to demonstrate all methodology and guide to proper usage of all methods. Presents a brief survey of spatial and spatio-temporal models, highlighting the Gaussian case and the binary data setting, along with the different methodologies for estimation and model fitting for these two data structures. Discusses models that allow for anisotropic and nonseparable behaviour in covariance functions in the spatial, spatio-temporal and multivariate settings. Gives an introduction to point pattern models, including testing for randomness, and fitting regular and clustered point patterns. The importance and assessment of isotropy of point patterns is detailed. Statisticians, researchers, and data analysts working with spatial and space-time data will benefit from this book as well as will graduate students with a background in basic statistics following courses in engineering, quantitative ecology or atmospheric science.

Alfred Bartolucci Introduction to Statistical Analysis of Laboratory Data Alfred Bartolucci Introduction to Statistical Analysis of Laboratory Data
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Alfred Bartolucci Introduction to Statistical Analysis of Laboratory Data


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9373.54 руб.

Introduction to Statistical Analysis of Laboratory Data presents a detailed discussion of important statistical concepts and methods of data presentation and analysis Provides detailed discussions on statistical applications including a comprehensive package of statistical tools that are specific to the laboratory experiment process Introduces terminology used in many applications such as the interpretation of assay design and validation as well as “fit for purpose” procedures including real world examples Includes a rigorous review of statistical quality control procedures in laboratory methodologies and influences on capabilities Presents methodologies used in the areas such as method comparison procedures, limit and bias detection, outlier analysis and detecting sources of variation Analysis of robustness and ruggedness including multivariate influences on response are introduced to account for controllable/uncontrollable laboratory conditions

Christensen William F. Methods of Multivariate Analysis Christensen William F. Methods of Multivariate Analysis
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Christensen William F. Methods of Multivariate Analysis


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10812.96 руб.

Praise for the Second Edition «This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere.» —IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a «methods» approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life situations. This Third Edition continues to explore the key descriptive and inferential procedures that result from multivariate analysis. Following a brief overview of the topic, the book goes on to review the fundamentals of matrix algebra, sampling from multivariate populations, and the extension of common univariate statistical procedures (including t-tests, analysis of variance, and multiple regression) to analogous multivariate techniques that involve several dependent variables. The latter half of the book describes statistical tools that are uniquely multivariate in nature, including procedures for discriminating among groups, characterizing low-dimensional latent structure in high-dimensional data, identifying clusters in data, and graphically illustrating relationships in low-dimensional space. In addition, the authors explore a wealth of newly added topics, including: Confirmatory Factor Analysis Classification Trees Dynamic Graphics Transformations to Normality Prediction for Multivariate Multiple Regression Kronecker Products and Vec Notation New exercises have been added throughout the book, allowing readers to test their comprehension of the presented material. Detailed appendices provide partial solutions as well as supplemental tables, and an accompanying FTP site features the book's data sets and related SAS® code. Requiring only a basic background in statistics, Methods of Multivariate Analysis, Third Edition is an excellent book for courses on multivariate analysis and applied statistics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for both statisticians and researchers across a wide variety of disciplines.

Xie Lei Advances in Statistical Monitoring of Complex Multivariate Processes. With Applications in Industrial Process Control Xie Lei Advances in Statistical Monitoring of Complex Multivariate Processes. With Applications in Industrial Process Control
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Xie Lei Advances in Statistical Monitoring of Complex Multivariate Processes. With Applications in Industrial Process Control


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7701.32 руб.

The development and application of multivariate statistical techniques in process monitoring has gained substantial interest over the past two decades in academia and industry alike. Initially developed for monitoring and fault diagnosis in complex systems, such techniques have been refined and applied in various engineering areas, for example mechanical and manufacturing, chemical, electrical and electronic, and power engineering. The recipe for the tremendous interest in multivariate statistical techniques lies in its simplicity and adaptability for developing monitoring applications. In contrast, competitive model, signal or knowledge based techniques showed their potential only whenever cost-benefit economics have justified the required effort in developing applications. Statistical Monitoring of Complex Multivariate Processes presents recent advances in statistics based process monitoring, explaining how these processes can now be used in areas such as mechanical and manufacturing engineering for example, in addition to the traditional chemical industry. This book: Contains a detailed theoretical background of the component technology. Brings together a large body of work to address the field’s drawbacks, and develops methods for their improvement. Details cross-disciplinary utilization, exemplified by examples in chemical, mechanical and manufacturing engineering. Presents real life industrial applications, outlining deficiencies in the methodology and how to address them. Includes numerous examples, tutorial questions and homework assignments in the form of individual and team-based projects, to enhance the learning experience. Features a supplementary website including Matlab algorithms and data sets. This book provides a timely reference text to the rapidly evolving area of multivariate statistical analysis for academics, advanced level students, and practitioners alike.

Daniel Denis J. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics Daniel Denis J. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics
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Daniel Denis J. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics


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8103.24 руб.

Enables readers to start doing actual data analysis fast for a truly hands-on learning experience This concise and very easy-to-use primer introduces readers to a host of computational tools useful for making sense out of data, whether that data come from the social, behavioral, or natural sciences. The book places great emphasis on both data analysis and drawing conclusions from empirical observations. It also provides formulas where needed in many places, while always remaining focused on concepts rather than mathematical abstraction. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics offers a variety of popular statistical analyses and data management tasks using SPSS that readers can immediately apply as needed for their own research, and emphasizes many helpful computational tools used in the discovery of empirical patterns. The book begins with a review of essential statistical principles before introducing readers to SPSS. The book then goes on to offer chapters on: Exploratory Data Analysis, Basic Statistics, and Visual Displays; Data Management in SPSS; Inferential Tests on Correlations, Counts, and Means; Power Analysis and Estimating Sample Size; Analysis of Variance – Fixed and Random Effects; Repeated Measures ANOVA; Simple and Multiple Linear Regression; Logistic Regression; Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis; Principal Components Analysis; Exploratory Factor Analysis; and Non-Parametric Tests. This helpful resource allows readers to: Understand data analysis in practice rather than delving too deeply into abstract mathematical concepts Make use of computational tools used by data analysis professionals. Focus on real-world application to apply concepts from the book to actual research Assuming only minimal, prior knowledge of statistics, SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics is an excellent “how-to” book for undergraduate and graduate students alike. This book is also a welcome resource for researchers and professionals who require a quick, go-to source for performing essential statistical analyses and data management tasks.

Tsiptsis Konstantinos K. Data Mining Techniques in CRM. Inside Customer Segmentation Tsiptsis Konstantinos K. Data Mining Techniques in CRM. Inside Customer Segmentation
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Tsiptsis Konstantinos K. Data Mining Techniques in CRM. Inside Customer Segmentation


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7619.64 руб.

This is an applied handbook for the application of data mining techniques in the CRM framework. It combines a technical and a business perspective to cover the needs of business users who are looking for a practical guide on data mining. It focuses on Customer Segmentation and presents guidelines for the development of actionable segmentation schemes. By using non-technical language it guides readers through all the phases of the data mining process.

Upcraft M. Lee Assessment Methods for Student Affairs Upcraft M. Lee Assessment Methods for Student Affairs
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Upcraft M. Lee Assessment Methods for Student Affairs


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3578.39 руб.

Editor John Schuh and his fellow contributors, all experts in the field, detail the methodological aspects of conducting assessment projects specifically for the student affairs practitioner who is ready to conduct assessment projects, but is not quite sure how to manage their technical aspects. Using a variety of case studies and concrete examples to illustrate various assessment approaches, the authors lead the reader step-by-step through each phase of the assessment process with jargon-free, hands-on guidance.

John Lachin M. Biostatistical Methods. The Assessment of Relative Risks John Lachin M. Biostatistical Methods. The Assessment of Relative Risks
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John Lachin M. Biostatistical Methods. The Assessment of Relative Risks


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12524.36 руб.

Praise for the First Edition «. . . an excellent textbook . . . an indispensable reference for biostatisticians and epidemiologists.» —International Statistical Institute A new edition of the definitive guide to classical and modern methods of biostatistics Biostatistics consists of various quantitative techniques that are essential to the description and evaluation of relationships among biologic and medical phenomena. Biostatistical Methods: The Assessment of Relative Risks, Second Edition develops basic concepts and derives an expanded array of biostatistical methods through the application of both classical statistical tools and more modern likelihood-based theories. With its fluid and balanced presentation, the book guides readers through the important statistical methods for the assessment of absolute and relative risks in epidemiologic studies and clinical trials with categorical, count, and event-time data. Presenting a broad scope of coverage and the latest research on the topic, the author begins with categorical data analysis methods for cross-sectional, prospective, and retrospective studies of binary, polychotomous, and ordinal data. Subsequent chapters present modern model-based approaches that include unconditional and conditional logistic regression; Poisson and negative binomial models for count data; and the analysis of event-time data including the Cox proportional hazards model and its generalizations. The book now includes an introduction to mixed models with fixed and random effects as well as expanded methods for evaluation of sample size and power. Additional new topics featured in this Second Edition include: Establishing equivalence and non-inferiority Methods for the analysis of polychotomous and ordinal data, including matched data and the Kappa agreement index Multinomial logistic for polychotomous data and proportional odds models for ordinal data Negative binomial models for count data as an alternative to the Poisson model GEE models for the analysis of longitudinal repeated measures and multivariate observations Throughout the book, SAS is utilized to illustrate applications to numerous real-world examples and case studies. A related website features all the data used in examples and problem sets along with the author's SAS routines. Biostatistical Methods, Second Edition is an excellent book for biostatistics courses at the graduate level. It is also an invaluable reference for biostatisticians, applied statisticians, and epidemiologists.

William Stimson A. Forensic Systems Engineering. Evaluating Operations by Discovery William Stimson A. Forensic Systems Engineering. Evaluating Operations by Discovery
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William Stimson A. Forensic Systems Engineering. Evaluating Operations by Discovery


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12602.15 руб.

A systems-level approach to reducing liability through process improvement Forensic Systems Analysis: Evaluating Operations by Discovery presents a systematic framework for uncovering and resolving problematic process failures. Carefully building the causal relationship from process to product, the discussion lays out in significant detail the appropriate and tactical approaches necessary to the pursuit of litigation with respect to corporate operations. Systemic process failures are addressed by flipping process improvement models to study both improvement and failure, resulting in arguments and methodologies relevant to any product or service industry. Guidance on risk analysis of operations combines evaluation of process control, stability, capability, verification, validation, specification, product reliability, serial dependence, and more, providing a robust framework with which to target large-scale nonconforming products and services. Relevant to anyone involved in business, manufacturing, service, and control, this book: Covers process liability and operations management from both engineering and legal perspectives Offers analyses that present novel uses of traditional engineering methods concerning risk and product quality and reliability Takes a rigorous approach to system tactics and constraints related to product and service operations and identifies dysfunctional processes Offers both prescriptive and descriptive solutions to both the plaintiff and the defendant The global economy has created an environment in which huge production volume, complex data bases, and multiple dispersed suppliers greatly challenge industrial operations. This informative guide provides a practical blueprint for uncovering problematic process failures.

Alan Agresti Categorical Data Analysis Alan Agresti Categorical Data Analysis
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Alan Agresti Categorical Data Analysis


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11668.66 руб.

Praise for the Second Edition «A must-have book for anyone expecting to do research and/or applications in categorical data analysis.» —Statistics in Medicine «It is a total delight reading this book.» —Pharmaceutical Research «If you do any analysis of categorical data, this is an essential desktop reference.» —Technometrics The use of statistical methods for analyzing categorical data has increased dramatically, particularly in the biomedical, social sciences, and financial industries. Responding to new developments, this book offers a comprehensive treatment of the most important methods for categorical data analysis. Categorical Data Analysis, Third Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial loglinear models for discrete data with normal regression for continuous data. This edition also features: An emphasis on logistic and probit regression methods for binary, ordinal, and nominal responses for independent observations and for clustered data with marginal models and random effects models Two new chapters on alternative methods for binary response data, including smoothing and regularization methods, classification methods such as linear discriminant analysis and classification trees, and cluster analysis New sections introducing the Bayesian approach for methods in that chapter More than 100 analyses of data sets and over 600 exercises Notes at the end of each chapter that provide references to recent research and topics not covered in the text, linked to a bibliography of more than 1,200 sources A supplementary website showing how to use R and SAS; for all examples in the text, with information also about SPSS and Stata and with exercise solutions Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and methodologists, such as biostatisticians and researchers in the social and behavioral sciences, medicine and public health, marketing, education, finance, biological and agricultural sciences, and industrial quality control.


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Praise for the First Edition «. . . an excellent textbook . . . an indispensable reference for biostatisticians and epidemiologists.» —International Statistical Institute A new edition of the definitive guide to classical and modern methods of biostatistics Biostatistics consists of various quantitative techniques that are essential to the description and evaluation of relationships among biologic and medical phenomena. Biostatistical Methods: The Assessment of Relative Risks, Second Edition develops basic concepts and derives an expanded array of biostatistical methods through the application of both classical statistical tools and more modern likelihood-based theories. With its fluid and balanced presentation, the book guides readers through the important statistical methods for the assessment of absolute and relative risks in epidemiologic studies and clinical trials with categorical, count, and event-time data. Presenting a broad scope of coverage and the latest research on the topic, the author begins with categorical data analysis methods for cross-sectional, prospective, and retrospective studies of binary, polychotomous, and ordinal data. Subsequent chapters present modern model-based approaches that include unconditional and conditional logistic regression; Poisson and negative binomial models for count data; and the analysis of event-time data including the Cox proportional hazards model and its generalizations. The book now includes an introduction to mixed models with fixed and random effects as well as expanded methods for evaluation of sample size and power. Additional new topics featured in this Second Edition include: Establishing equivalence and non-inferiority Methods for the analysis of polychotomous and ordinal data, including matched data and the Kappa agreement index Multinomial logistic for polychotomous data and proportional odds models for ordinal data Negative binomial models for count data as an alternative to the Poisson model GEE models for the analysis of longitudinal repeated measures and multivariate observations Throughout the book, SAS is utilized to illustrate applications to numerous real-world examples and case studies. A related website features all the data used in examples and problem sets along with the author's SAS routines. Biostatistical Methods, Second Edition is an excellent book for biostatistics courses at the graduate level. It is also an invaluable reference for biostatisticians, applied statisticians, and epidemiologists.
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