- Table View
- List View
Understanding Statistical Error: A Primer for Biologists
by Marek GierlinskiThis accessible introductory textbook provides a straightforward, practical explanation of how statistical analysis and error measurements should be applied in biological research. Understanding Statistical Error - A Primer for Biologists: Introduces the essential topic of error analysis to biologists Contains mathematics at a level that all biologists can grasp Presents the formulas required to calculate each confidence interval for use in practice Is based on a successful series of lectures from the author’s established course Assuming no prior knowledge of statistics, this book covers the central topics needed for efficient data analysis, ranging from probability distributions, statistical estimators, confidence intervals, error propagation and uncertainties in linear regression, to advice on how to use error bars in graphs properly. Using simple mathematics, all these topics are carefully explained and illustrated with figures and worked examples. The emphasis throughout is on visual representation and on helping the reader to approach the analysis of experimental data with confidence. This useful guide explains how to evaluate uncertainties of key parameters, such as the mean, median, proportion and correlation coefficient. Crucially, the reader will also learn why confidence intervals are important and how they compare against other measures of uncertainty. Understanding Statistical Error - A Primer for Biologists can be used both by students and researchers to deepen their knowledge and find practical formulae to carry out error analysis calculations. It is a valuable guide for students, experimental biologists and professional researchers in biology, biostatistics, computational biology, cell and molecular biology, ecology, biological chemistry, drug discovery, biophysics, as well as wider subjects within life sciences and any field where error analysis is required.
Understanding the Mathematics of Personal Finance: An Introduction to Financial Literacy
by Lawrence N. DworskyA user-friendly presentation of the essential concepts and tools for calculating real costs and profits in personal finance Understanding the Mathematics of Personal Finance explains how mathematics, a simple calculator, and basic computer spreadsheets can be used to break down and understand even the most complex loan structures. In an easy-to-follow style, the book clearly explains the workings of basic financial calculations, captures the concepts behind loans and interest in a step-by-step manner, and details how these steps can be implemented for practical purposes. Rather than simply providing investment and borrowing strategies, the author successfully equips readers with the skills needed to make accurate and effective decisions in all aspects of personal finance ventures, including mortgages, annuities, life insurance, and credit card debt. The book begins with a primer on mathematics, covering the basics of arithmetic operations and notations, and proceeds to explore the concepts of interest, simple interest, and compound interest. Subsequent chapters illustrate the application of these concepts to common types of personal finance exchanges, including: Loan amortization and savings Mortgages, reverse mortgages, and viatical settlements Prepayment penalties Credit cards The book provides readers with the tools needed to calculate real costs and profits using various financial instruments. Mathematically inclined readers will enjoy the inclusion of mathematical derivations, but these sections are visually distinct from the text and can be skipped without the loss of content or complete understanding of the material. In addition, references to online calculators and instructions for building the calculations involved in a spreadsheet are provided. Furthermore, a related Web site features additional problem sets, the spreadsheet calculators that are referenced and used throughout the book, and links to various other financial calculators. Understanding the Mathematics of Personal Finance is an excellent book for finance courses at the undergraduate level. It is also an essential reference for individuals who are interested in learning how to make effective financial decisions in their everyday lives.
Understanding Uncertainty (Wiley Series in Probability and Statistics)
by Dennis V. LindleyPraise for the First Edition "...a reference for everyone who is interested in knowing and handling uncertainty." —Journal of Applied Statistics The critically acclaimed First Edition of Understanding Uncertainty provided a study of uncertainty addressed to scholars in all fields, showing that uncertainty could be measured by probability, and that probability obeyed three basic rules that enabled uncertainty to be handled sensibly in everyday life. These ideas were extended to embrace the scientific method and to show how decisions, containing an uncertain element, could be rationally made. Featuring new material, the Revised Edition remains the go-to guide for uncertainty and decision making, providing further applications at an accessible level including: A critical study of transitivity, a basic concept in probability A discussion of how the failure of the financial sector to use the proper approach to uncertainty may have contributed to the recent recession A consideration of betting, showing that a bookmaker's odds are not expressions of probability Applications of the book’s thesis to statistics A demonstration that some techniques currently popular in statistics, like significance tests, may be unsound, even seriously misleading, because they violate the rules of probability Understanding Uncertainty, Revised Edition is ideal for students studying probability or statistics and for anyone interested in one of the most fascinating and vibrant fields of study in contemporary science and mathematics.
Understanding Uncertainty (Wiley Series in Probability and Statistics)
by Dennis V. LindleyPraise for the First Edition "...a reference for everyone who is interested in knowing and handling uncertainty." —Journal of Applied Statistics The critically acclaimed First Edition of Understanding Uncertainty provided a study of uncertainty addressed to scholars in all fields, showing that uncertainty could be measured by probability, and that probability obeyed three basic rules that enabled uncertainty to be handled sensibly in everyday life. These ideas were extended to embrace the scientific method and to show how decisions, containing an uncertain element, could be rationally made. Featuring new material, the Revised Edition remains the go-to guide for uncertainty and decision making, providing further applications at an accessible level including: A critical study of transitivity, a basic concept in probability A discussion of how the failure of the financial sector to use the proper approach to uncertainty may have contributed to the recent recession A consideration of betting, showing that a bookmaker's odds are not expressions of probability Applications of the book’s thesis to statistics A demonstration that some techniques currently popular in statistics, like significance tests, may be unsound, even seriously misleading, because they violate the rules of probability Understanding Uncertainty, Revised Edition is ideal for students studying probability or statistics and for anyone interested in one of the most fascinating and vibrant fields of study in contemporary science and mathematics.
Undocumented Migration
by Roberto G. Gonzales Nando Sigona Martha C. Franco Anna PapoutsiUndocumented migration is a global and yet elusive phenomenon. Despite contemporary efforts to patrol national borders and mass deportation programs, it remains firmly placed at the top of the political agenda in many countries where it receives hostile media coverage and generates fierce debate. However, as this much-needed book makes clear, unauthorized movement should not be confused or crudely assimilated with the social reality of growing numbers of large, settled populations lacking full citizenship and experiencing precarious lives. From the journeys migrants take to the lives they seek on arrival and beyond, Undocumented Migration provides a comparative view of how this phenomenon plays out, looking in particular at the United States and Europe. Drawing on their extensive expertise, the authors breathe life into the various issues and debates surrounding migration, including the experiences and voices of migrants themselves, to offer a critical analysis of a hidden and too often misrepresented population.
Univariate, Bivariate, and Multivariate Statistics Using R: Quantitative Tools for Data Analysis and Data Science
by Daniel J. DenisA practical source for performing essential statistical analyses and data management tasks in R Univariate, Bivariate, and Multivariate Statistics Using R offers a practical and very user-friendly introduction to the use of R software that covers a range of statistical methods featured in data analysis and data science. The author— a noted expert in quantitative teaching —has written a quick go-to reference for performing essential statistical analyses and data management tasks in R. Requiring only minimal prior knowledge, the book introduces concepts needed for an immediate yet clear understanding of statistical concepts essential to interpreting software output. The author explores univariate, bivariate, and multivariate statistical methods, as well as select nonparametric tests. Altogether a hands-on manual on the applied statistics and essential R computing capabilities needed to write theses, dissertations, as well as research publications. The book is comprehensive in its coverage of univariate through to multivariate procedures, while serving as a friendly and gentle introduction to R software for the newcomer. This important resource: Offers an introductory, concise guide to the computational tools that are useful for making sense out of data using R statistical software Provides a resource for students and professionals in the social, behavioral, and natural sciences Puts the emphasis on the computational tools used in the discovery of empirical patterns Features a variety of popular statistical analyses and data management tasks that can be immediately and quickly applied as needed to research projects Shows how to apply statistical analysis using R to data sets in order to get started quickly performing essential tasks in data analysis and data science Written for students, professionals, and researchers primarily in the social, behavioral, and natural sciences, Univariate, Bivariate, and Multivariate Statistics Using R offers an easy-to-use guide for performing data analysis fast, with an emphasis on drawing conclusions from empirical observations. The book can also serve as a primary or secondary textbook for courses in data analysis or data science, or others in which quantitative methods are featured.
Univariate, Bivariate, and Multivariate Statistics Using R: Quantitative Tools for Data Analysis and Data Science
by Daniel J. DenisA practical source for performing essential statistical analyses and data management tasks in R Univariate, Bivariate, and Multivariate Statistics Using R offers a practical and very user-friendly introduction to the use of R software that covers a range of statistical methods featured in data analysis and data science. The author— a noted expert in quantitative teaching —has written a quick go-to reference for performing essential statistical analyses and data management tasks in R. Requiring only minimal prior knowledge, the book introduces concepts needed for an immediate yet clear understanding of statistical concepts essential to interpreting software output. The author explores univariate, bivariate, and multivariate statistical methods, as well as select nonparametric tests. Altogether a hands-on manual on the applied statistics and essential R computing capabilities needed to write theses, dissertations, as well as research publications. The book is comprehensive in its coverage of univariate through to multivariate procedures, while serving as a friendly and gentle introduction to R software for the newcomer. This important resource: Offers an introductory, concise guide to the computational tools that are useful for making sense out of data using R statistical software Provides a resource for students and professionals in the social, behavioral, and natural sciences Puts the emphasis on the computational tools used in the discovery of empirical patterns Features a variety of popular statistical analyses and data management tasks that can be immediately and quickly applied as needed to research projects Shows how to apply statistical analysis using R to data sets in order to get started quickly performing essential tasks in data analysis and data science Written for students, professionals, and researchers primarily in the social, behavioral, and natural sciences, Univariate, Bivariate, and Multivariate Statistics Using R offers an easy-to-use guide for performing data analysis fast, with an emphasis on drawing conclusions from empirical observations. The book can also serve as a primary or secondary textbook for courses in data analysis or data science, or others in which quantitative methods are featured.
Univariate Discrete Distributions (Wiley Series in Probability and Statistics #444)
by Norman L. Johnson Adrienne W. Kemp Samuel KotzThis Set Contains: Continuous Multivariate Distributions, Volume 1, Models and Applications, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Continuous Univariate Distributions, Volume 1, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Continuous Univariate Distributions, Volume 2, 2nd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Discrete Multivariate Distributions by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Univariate Discrete Distributions, 3rd Edition by Samuel Kotz, N. Balakrishnan and Normal L. Johnson Discover the latest advances in discrete distributions theory The Third Edition of the critically acclaimed Univariate Discrete Distributions provides a self-contained, systematic treatment of the theory, derivation, and application of probability distributions for count data. Generalized zeta-function and q-series distributions have been added and are covered in detail. New families of distributions, including Lagrangian-type distributions, are integrated into this thoroughly revised and updated text. Additional applications of univariate discrete distributions are explored to demonstrate the flexibility of this powerful method. A thorough survey of recent statistical literature draws attention to many new distributions and results for the classical distributions. Approximately 450 new references along with several new sections are introduced to reflect the current literature and knowledge of discrete distributions. Beginning with mathematical, probability, and statistical fundamentals, the authors provide clear coverage of the key topics in the field, including: Families of discrete distributions Binomial distribution Poisson distribution Negative binomial distribution Hypergeometric distributions Logarithmic and Lagrangian distributions Mixture distributions Stopped-sum distributions Matching, occupancy, runs, and q-series distributions Parametric regression models and miscellanea Emphasis continues to be placed on the increasing relevance of Bayesian inference to discrete distribution, especially with regard to the binomial and Poisson distributions. New derivations of discrete distributions via stochastic processes and random walks are introduced without unnecessarily complex discussions of stochastic processes. Throughout the Third Edition, extensive information has been added to reflect the new role of computer-based applications. With its thorough coverage and balanced presentation of theory and application, this is an excellent and essential reference for statisticians and mathematicians.
A User's Guide to Principal Components (Wiley Series in Probability and Statistics #587)
by J. Edward JacksonWILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. From the Reviews of A User’s Guide to Principal Components "The book is aptly and correctly named–A User’s Guide. It is the kind of book that a user at any level, novice or skilled practitioner, would want to have at hand for autotutorial, for refresher, or as a general-purpose guide through the maze of modern PCA." –Technometrics "I recommend A User’s Guide to Principal Components to anyone who is running multivariate analyses, or who contemplates performing such analyses. Those who write their own software will find the book helpful in designing better programs. Those who use off-the-shelf software will find it invaluable in interpreting the results." –Mathematical Geology
Using Excel for Principles of Econometrics
by Genevieve Briand R. Carter HillPrinciples of Econometrics is an introductory book for undergraduate students in economics and finance, and can be used for MBA and first-year graduate students in many fields. The 4th Edition provides students with an understanding of why econometrics is necessary and a working knowledge of basic econometric tools. This text emphasizes motivation, understanding and implementation by introducing very simple economic models and asking economic questions that students can answer.
Using Predictive Analytics to Improve Healthcare Outcomes
by John W. Nelson Jayne Felgen Mary Ann HozakUsing Predictive Analytics to Improve Healthcare Outcomes Discover a comprehensive overview, from established leaders in the field, of how to use predictive analytics and other analytic methods for healthcare quality improvement.Using Predictive Analytics to Improve Healthcare Outcomes delivers a 16-step process to use predictive analytics to improve operations in the complex industry of healthcare. The book includes numerous case studies that make use of predictive analytics and other mathematical methodologies to save money and improve patient outcomes. The book is organized as a “how-to” manual, showing how to use existing theory and tools to achieve desired positive outcomes.You will learn how your organization can use predictive analytics to identify the most impactful operational interventions before changing operations. This includes: A thorough introduction to data, caring theory, Relationship-Based Care®, the Caring Behaviors Assurance System©, and healthcare operations, including how to build a measurement model and improve organizational outcomes. An exploration of analytics in action, including comprehensive case studies on patient falls, palliative care, infection reduction, reducing rates of readmission for heart failure, and more—all resulting in action plans allowing clinicians to make changes that have been proven in advance to result in positive outcomes.Discussions of how to refine quality improvement initiatives, including the use of “comfort” as a construct to illustrate the importance of solid theory and good measurement in adequate pain management.An examination of international organizations using analytics to improve operations within cultural context.Using Predictive Analytics to Improve Healthcare Outcomes is perfect for executives, researchers, and quality improvement staff at healthcare organizations, as well as educators teaching mathematics, data science, or quality improvement. Employ this valuable resource that walks you through the steps of managing and optimizing outcomes in your clinical care operations.
Using Predictive Analytics to Improve Healthcare Outcomes
by John W. NelsonUsing Predictive Analytics to Improve Healthcare Outcomes Discover a comprehensive overview, from established leaders in the field, of how to use predictive analytics and other analytic methods for healthcare quality improvement.Using Predictive Analytics to Improve Healthcare Outcomes delivers a 16-step process to use predictive analytics to improve operations in the complex industry of healthcare. The book includes numerous case studies that make use of predictive analytics and other mathematical methodologies to save money and improve patient outcomes. The book is organized as a “how-to” manual, showing how to use existing theory and tools to achieve desired positive outcomes.You will learn how your organization can use predictive analytics to identify the most impactful operational interventions before changing operations. This includes: A thorough introduction to data, caring theory, Relationship-Based Care®, the Caring Behaviors Assurance System©, and healthcare operations, including how to build a measurement model and improve organizational outcomes. An exploration of analytics in action, including comprehensive case studies on patient falls, palliative care, infection reduction, reducing rates of readmission for heart failure, and more—all resulting in action plans allowing clinicians to make changes that have been proven in advance to result in positive outcomes.Discussions of how to refine quality improvement initiatives, including the use of “comfort” as a construct to illustrate the importance of solid theory and good measurement in adequate pain management.An examination of international organizations using analytics to improve operations within cultural context.Using Predictive Analytics to Improve Healthcare Outcomes is perfect for executives, researchers, and quality improvement staff at healthcare organizations, as well as educators teaching mathematics, data science, or quality improvement. Employ this valuable resource that walks you through the steps of managing and optimizing outcomes in your clinical care operations.
Using SAS for Econometrics
by R. Carter Hill Randall C. CampbellA supplement such as Using SAS for Econometrics is quite essential for use in a classroom environment, for those attempting to learn SAS, and for quick and useful reference. The SAS documentation comes in many volumes, and several are thousands of pages long. This makes for a very difficult challenge when getting started with SAS. This volume spans several levels of econometrics. It is suitable for undergraduate students who will use canned SAS statistical procedures, and for graduate students who will use advanced procedures as well as direct programming in SAS's matrix language, discussed in chapter appendices. Material within the chapters is accessible to undergraduate and/or Masters students, with appendices to chapters devoted to more advanced materials and matrix programming.
Using Stata for Principles of Econometrics
by Lee C. Adkins R. Carter HillThis is the Using Stata text for Principles of Econometrics, 4th Edition.Principles of Econometrics is an introductory book for undergraduate students in economics and finance, and can be used for MBA and first-year graduate students in many fields. The 4th Edition provides students with an understanding of why econometrics is necessary and a working knowledge of basic econometric tools. This text emphasizes motivation, understanding and implementation by introducing very simple economic models and asking economic questions that students can answer.
Using Statistics in the Social and Health Sciences with SPSS and Excel
by Martin Lee AbbottProvides a step-by-step approach to statistical procedures to analyze data and conduct research, with detailed sections in each chapter explaining SPSS® and Excel® applications This book identifies connections between statistical applications and research design using cases, examples, and discussion of specific topics from the social and health sciences. Researched and class-tested to ensure an accessible presentation, the book combines clear, step-by-step explanations for both the novice and professional alike to understand the fundamental statistical practices for organizing, analyzing, and drawing conclusions from research data in their field. The book begins with an introduction to descriptive and inferential statistics and then acquaints readers with important features of statistical applications (SPSS and Excel) that support statistical analysis and decision making. Subsequent chapters treat the procedures commonly employed when working with data across various fields of social science research. Individual chapters are devoted to specific statistical procedures, each ending with lab application exercises that pose research questions, examine the questions through their application in SPSS and Excel, and conclude with a brief research report that outlines key findings drawn from the results. Real-world examples and data from social and health sciences research are used throughout the book, allowing readers to reinforce their comprehension of the material. Using Statistics in the Social and Health Sciences with SPSS® and Excel® includes: Use of straightforward procedures and examples that help students focus on understanding of analysis and interpretation of findings Inclusion of a data lab section in each chapter that provides relevant, clear examples Introduction to advanced statistical procedures in chapter sections (e.g., regression diagnostics) and separate chapters (e.g., multiple linear regression) for greater relevance to real-world research needs Emphasizing applied statistical analyses, this book can serve as the primary text in undergraduate and graduate university courses within departments of sociology, psychology, urban studies, health sciences, and public health, as well as other related departments. It will also be useful to statistics practitioners through extended sections using SPSS® and Excel® for analyzing data.
Using Statistics in the Social and Health Sciences with SPSS and Excel
by Martin Lee AbbottProvides a step-by-step approach to statistical procedures to analyze data and conduct research, with detailed sections in each chapter explaining SPSS® and Excel® applications This book identifies connections between statistical applications and research design using cases, examples, and discussion of specific topics from the social and health sciences. Researched and class-tested to ensure an accessible presentation, the book combines clear, step-by-step explanations for both the novice and professional alike to understand the fundamental statistical practices for organizing, analyzing, and drawing conclusions from research data in their field. The book begins with an introduction to descriptive and inferential statistics and then acquaints readers with important features of statistical applications (SPSS and Excel) that support statistical analysis and decision making. Subsequent chapters treat the procedures commonly employed when working with data across various fields of social science research. Individual chapters are devoted to specific statistical procedures, each ending with lab application exercises that pose research questions, examine the questions through their application in SPSS and Excel, and conclude with a brief research report that outlines key findings drawn from the results. Real-world examples and data from social and health sciences research are used throughout the book, allowing readers to reinforce their comprehension of the material. Using Statistics in the Social and Health Sciences with SPSS® and Excel® includes: Use of straightforward procedures and examples that help students focus on understanding of analysis and interpretation of findings Inclusion of a data lab section in each chapter that provides relevant, clear examples Introduction to advanced statistical procedures in chapter sections (e.g., regression diagnostics) and separate chapters (e.g., multiple linear regression) for greater relevance to real-world research needs Emphasizing applied statistical analyses, this book can serve as the primary text in undergraduate and graduate university courses within departments of sociology, psychology, urban studies, health sciences, and public health, as well as other related departments. It will also be useful to statistics practitioners through extended sections using SPSS® and Excel® for analyzing data.
Using Technology to Enhance Clinical Supervision
by Tony Rousmaniere Edina Renfro-MichelThis is the first comprehensive research and practice-based guide for understanding and assessing supervision technology and for using it to improve the breadth and depth of services offered to supervisees and clients. Written by supervisors, for supervisors, it examines the technology that is currently available and how and when to use it. Part I provides a thorough review of the technological, legal, ethical, cultural, accessibility, and security competencies that are the foundation for effectively integrating technology into clinical supervision. Part II presents applications of the most prominent and innovative uses of technology across the major domains in counseling, along with best practices for delivery. Each chapter in this section contains a literature review, concrete examples for use, case examples, and lessons learned. *Requests for digital versions from the ACA can be found on wiley.com. *To request print copies, please visit the ACA website here. *Reproduction requests for material from books published by ACA should be directed to permissions@counseling.org
Using Technology to Enhance Clinical Supervision
by Tony Rousmaniere Edina Renfro-MichelThis is the first comprehensive research and practice-based guide for understanding and assessing supervision technology and for using it to improve the breadth and depth of services offered to supervisees and clients. Written by supervisors, for supervisors, it examines the technology that is currently available and how and when to use it. Part I provides a thorough review of the technological, legal, ethical, cultural, accessibility, and security competencies that are the foundation for effectively integrating technology into clinical supervision. Part II presents applications of the most prominent and innovative uses of technology across the major domains in counseling, along with best practices for delivery. Each chapter in this section contains a literature review, concrete examples for use, case examples, and lessons learned. *Requests for digital versions from the ACA can be found on wiley.com. *To request print copies, please visit the ACA website here. *Reproduction requests for material from books published by ACA should be directed to permissions@counseling.org
Using the Weibull Distribution: Reliability, Modeling, and Inference (Wiley Series in Probability and Statistics #950)
by John I. McCoolUnderstand and utilize the latest developments in Weibull inferential methods While the Weibull distribution is widely used in science and engineering, most engineers do not have the necessary statistical training to implement the methodology effectively. Using the Weibull Distribution: Reliability, Modeling, and Inference fills a gap in the current literature on the topic, introducing a self-contained presentation of the probabilistic basis for the methodology while providing powerful techniques for extracting information from data. The author explains the use of the Weibull distribution and its statistical and probabilistic basis, providing a wealth of material that is not available in the current literature. The book begins by outlining the fundamental probability and statistical concepts that serve as a foundation for subsequent topics of coverage, including: • Optimum burn-in, age and block replacement, warranties and renewal theory • Exact inference in Weibull regression • Goodness of fit testing and distinguishing the Weibull from the lognormal • Inference for the Three Parameter Weibull Throughout the book, a wealth of real-world examples showcases the discussed topics and each chapter concludes with a set of exercises, allowing readers to test their understanding of the presented material. In addition, a related website features the author's own software for implementing the discussed analyses along with a set of modules written in Mathcad®, and additional graphical interface software for performing simulations. With its numerous hands-on examples, exercises, and software applications, Using the Weibull Distribution is an excellent book for courses on quality control and reliability engineering at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for engineers, scientists, and business analysts who gather and interpret data that follows the Weibull distribution
Using the Weibull Distribution: Reliability, Modeling, and Inference (Wiley Series in Probability and Statistics #950)
by John I. McCoolUnderstand and utilize the latest developments in Weibull inferential methods While the Weibull distribution is widely used in science and engineering, most engineers do not have the necessary statistical training to implement the methodology effectively. Using the Weibull Distribution: Reliability, Modeling, and Inference fills a gap in the current literature on the topic, introducing a self-contained presentation of the probabilistic basis for the methodology while providing powerful techniques for extracting information from data. The author explains the use of the Weibull distribution and its statistical and probabilistic basis, providing a wealth of material that is not available in the current literature. The book begins by outlining the fundamental probability and statistical concepts that serve as a foundation for subsequent topics of coverage, including: • Optimum burn-in, age and block replacement, warranties and renewal theory • Exact inference in Weibull regression • Goodness of fit testing and distinguishing the Weibull from the lognormal • Inference for the Three Parameter Weibull Throughout the book, a wealth of real-world examples showcases the discussed topics and each chapter concludes with a set of exercises, allowing readers to test their understanding of the presented material. In addition, a related website features the author's own software for implementing the discussed analyses along with a set of modules written in Mathcad®, and additional graphical interface software for performing simulations. With its numerous hands-on examples, exercises, and software applications, Using the Weibull Distribution is an excellent book for courses on quality control and reliability engineering at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for engineers, scientists, and business analysts who gather and interpret data that follows the Weibull distribution
Value Creation in Management Accounting and Strategic Management: An Integrated Approach
by Satoshi Sugahara Nabyla Daidj Sumitaka UshioThis book explores two combined approaches (strategy and accounting) from a cross-disciplinary perspective in order to improve knowledge of value creation in various contexts. Existing studies on this topic have generally adopted a purely account-based or strategy-oriented approach to address this issue. However, this book draws upon a number of well-defined theoretical and empirical backgrounds and methodologies. Since the 1980s, many changes have occurred and companies have increasingly focused their strategies on value creation. Consequently, new strategic directions have emerged, especially for managerial accounting. Management accounting and alignment with strategy could thus improve performance. This book encourages further thought and reflection on these issues which should be pursued in the future as firms face new challenges associated with the acceleration of digital transformation.
Value Creation in Management Accounting and Strategic Management: An Integrated Approach
by Satoshi Sugahara Nabyla Daidj Sumitaka UshioThis book explores two combined approaches (strategy and accounting) from a cross-disciplinary perspective in order to improve knowledge of value creation in various contexts. Existing studies on this topic have generally adopted a purely account-based or strategy-oriented approach to address this issue. However, this book draws upon a number of well-defined theoretical and empirical backgrounds and methodologies. Since the 1980s, many changes have occurred and companies have increasingly focused their strategies on value creation. Consequently, new strategic directions have emerged, especially for managerial accounting. Management accounting and alignment with strategy could thus improve performance. This book encourages further thought and reflection on these issues which should be pursued in the future as firms face new challenges associated with the acceleration of digital transformation.
Variance Components (Wiley Series in Probability and Statistics #391)
by Shayle R. Searle George Casella Charles E. McCullochWILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. ". . .Variance Components is an excellent book. It is organized and well written, and provides many references to a variety of topics. I recommend it to anyone with interest in linear models." —Journal of the American Statistical Association "This book provides a broad coverage of methods for estimating variance components which appeal to students and research workers . . . The authors make an outstanding contribution to teaching and research in the field of variance component estimation." —Mathematical Reviews "The authors have done an excellent job in collecting materials on a broad range of topics. Readers will indeed gain from using this book . . . I must say that the authors have done a commendable job in their scholarly presentation." —Technometrics This book focuses on summarizing the variability of statistical data known as the analysis of variance table. Penned in a readable style, it provides an up-to-date treatment of research in the area. The book begins with the history of analysis of variance and continues with discussions of balanced data, analysis of variance for unbalanced data, predictions of random variables, hierarchical models and Bayesian estimation, binary and discrete data, and the dispersion mean model.
Variance-Constrained Multi-Objective Stochastic Control and Filtering (Wiley Series in Dynamics and Control of Electromechanical Systems)
by Lifeng Ma Zidong Wang Yuming BoUnifies existing and emerging concepts concerning multi-objective control and stochastic control with engineering-oriented phenomena Establishes a unified theoretical framework for control and filtering problems for a class of discrete-time nonlinear stochastic systems with consideration to performance Includes case studies of several nonlinear stochastic systems Investigates the phenomena of incomplete information, including missing/degraded measurements, actuator failures and sensor saturations Considers both time-invariant systems and time-varying systems Exploits newly developed techniques to handle the emerging mathematical and computational challenges
Variance-Constrained Multi-Objective Stochastic Control and Filtering (Wiley Series in Dynamics and Control of Electromechanical Systems)
by Lifeng Ma Zidong Wang Yuming BoUnifies existing and emerging concepts concerning multi-objective control and stochastic control with engineering-oriented phenomena Establishes a unified theoretical framework for control and filtering problems for a class of discrete-time nonlinear stochastic systems with consideration to performance Includes case studies of several nonlinear stochastic systems Investigates the phenomena of incomplete information, including missing/degraded measurements, actuator failures and sensor saturations Considers both time-invariant systems and time-varying systems Exploits newly developed techniques to handle the emerging mathematical and computational challenges