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Author: Peter McCullagh
Publisher: Springer Nature
ISBN: 3031142756
Category : Mathematics
Languages : en
Pages : 415
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Book Description
The first half of the book is aimed at quantitative research workers in biology, medicine, ecology and genetics. The book as a whole is aimed at graduate students in statistics, biostatistics, and other quantitative disciplines. Ten detailed examples show how the author approaches real-world statistical problems in a principled way that allows for adequate compromise and flexibility. The need to accommodate correlations associated with space, time and other relationships is a recurring theme, so variance-components models feature prominently. Statistical pitfalls are illustrated via examples taken from the recent scientific literature. Chapter 11 sets the scene, not just for the second half of the book, but for the book as a whole. It begins by defining fundamental concepts such as baseline, observational unit, experimental unit, covariates and relationships, randomization, treatment assignment, and the role that these play in model formulation. Compatibility of the model with the randomization scheme is crucial. The effect of treatment is invariably modelled as a group action on probability distributions. Technical matters connected with space-time covariance functions, residual likelihood, likelihood ratios, and transformations are discussed in later chapters.
Author: Peter McCullagh
Publisher: Springer Nature
ISBN: 3031142756
Category : Mathematics
Languages : en
Pages : 415
Get Book Here
Book Description
The first half of the book is aimed at quantitative research workers in biology, medicine, ecology and genetics. The book as a whole is aimed at graduate students in statistics, biostatistics, and other quantitative disciplines. Ten detailed examples show how the author approaches real-world statistical problems in a principled way that allows for adequate compromise and flexibility. The need to accommodate correlations associated with space, time and other relationships is a recurring theme, so variance-components models feature prominently. Statistical pitfalls are illustrated via examples taken from the recent scientific literature. Chapter 11 sets the scene, not just for the second half of the book, but for the book as a whole. It begins by defining fundamental concepts such as baseline, observational unit, experimental unit, covariates and relationships, randomization, treatment assignment, and the role that these play in model formulation. Compatibility of the model with the randomization scheme is crucial. The effect of treatment is invariably modelled as a group action on probability distributions. Technical matters connected with space-time covariance functions, residual likelihood, likelihood ratios, and transformations are discussed in later chapters.
Author:
Publisher:
ISBN:
Category : Electronic journals
Languages : en
Pages : 254
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Book Description
Author: Nair, Suja R.
Publisher: IGI Global
ISBN: 1522558381
Category : Education
Languages : en
Pages : 656
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Book Description
Ethics, entrepreneurship, and governance are very essential and crucial for the sustainable development of institutions of higher education, especially in the face of moral ambiguity or ethical lapses that could occur. As such, it is vital to explore how to facilitate the effective and efficient development of higher education institutions to put into practice ethical behaviors and entrepreneurial values for the progressive future of society. The Handbook of Research on Ethics, Entrepreneurship, and Governance in Higher Education is a pivotal reference source that provides vital research on the application of ethics, entrepreneurship, and governance in higher education institutions. Featuring coverage topics such academic misconduct, ethical leadership, and student values, this publication is ideally designed for educational administrators, professors, academicians, researchers, and graduate-level students seeking current research on the impact of globalization on the ethics and governance in higher education through various policy decisions and practices.
Author: Maureen Berner
Publisher: International City/County Management Association(ICMA)
ISBN: 9780873267717
Category : Business & Economics
Languages : en
Pages : 176
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Book Description
Author: Danette McGilvray
Publisher: Academic Press
ISBN: 0128180161
Category : Computers
Languages : en
Pages : 378
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Book Description
Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today's data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization's standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. - Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach - Contains real examples from around the world, gleaned from the author's consulting practice and from those who implemented based on her training courses and the earlier edition of the book - Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices - A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online
Author: Blankenship, Rebecca J.
Publisher: IGI Global
ISBN: 1522593330
Category : Education
Languages : en
Pages : 290
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Book Description
Colleges and universities throughout the United States are reimagining teaching and learning processes to best match the personalized needs of the 21st century learner in the present digital age. Applying various digital education strategies within undergraduate and graduate settings and identifying the metrics that can be used to effectively determine learning outcomes are all critical to ensuring a productive educational experience. Cases on Digital Learning and Teaching Transformations in Higher Education is an important resource to the field of education, especially within the TPACK construct, as it provides a glimpse into an initiative specifically designed to transform how university faculty design their courses for maximum and directed technology-relevant impact. Featuring an array of topics such as course transformation, digital retooling, technology trial and error, student engagement, and pedagogy, this book is ideal for university faculty, university administration, curriculum designers, instructional technology designers, academicians, and researchers.
Author: Abdul Quader Miah
Publisher: Springer
ISBN: 9811004013
Category : Social Science
Languages : en
Pages : 447
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Book Description
This book addresses the application of statistical techniques and methods across a wide range of disciplines. While its main focus is on the application of statistical methods, theoretical aspects are also provided as fundamental background information. It offers a systematic interpretation of results often discovered in general descriptions of methods and techniques such as linear and non-linear regression. SPSS is also used in all the application aspects. The presentation of data in the form of tables and graphs throughout the book not only guides users, but also explains the statistical application and assists readers in interpreting important features. The analysis of statistical data is presented consistently throughout the text. Academic researchers, practitioners and other users who work with statistical data will benefit from reading Applied Statistics for Social and Management Sciences.
Author:
Publisher:
ISBN:
Category : Computer industry
Languages : en
Pages : 240
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Book Description
Author: Erick Jones
Publisher: CRC Press
ISBN: 1439897824
Category : Business & Economics
Languages : en
Pages : 630
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Book Description
The next step in the evolution of the organizational quality field, Lean Six Sigma (LSS) has come of age. However, many challenges to using LSS in lieu of, in conjunction with, or integrated with other quality initiatives remain. An update on the current focus of quality management, Quality Management for Organizations Using Lean Six Sigma Techniques covers the concepts and principles of Lean Six Sigma and its origins in quality, total quality management (TQM), and statistical process control (SPC), and then explores how it can be integrated into manufacturing, logistics, and healthcare operations. The book presents the background on quality and Lean Six Sigma (LSS) techniques and tools, previous history of LSS in manufacturing, and current applications of LSS in operations such as logistics and healthcare. It provides a decision model for choosing whether to use LSS or other quality initiatives, which projects should be selected and prioritized, and what to do with non-LSS projects. The author also details an integration model for integrating and developing integrated LSS and other quality initiatives, and common mathematical techniques that you can use for performing LSS statistical calculations. He describes methods to attain the different Six Sigma certifications, and closes with discussion of future directions of Lean Six Sigma and quality. Case studies illustrate the integration of LSS principles into other quality initiatives, highlighting best practices as well as successful and failed integrations. This guide gives you a balanced description of the good, bad, and ugly in integrating LSS into modern operations, giving you the understanding necessary to immediately apply the concepts to your quality processes.
Author: Jon Howells
Publisher: Packt Publishing Ltd
ISBN: 1837638349
Category : Computers
Languages : en
Pages : 270
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Book Description
Bridge the gap between business and data science by learning how to interpret machine learning and AI models, manage data teams, and achieve impactful results Key Features Master the concepts of statistics and ML to interpret models and guide decisions Identify valuable AI use cases and manage data science projects from start to finish Empower top data science teams to solve complex problems and build AI products Purchase of the print Kindle book includes a free PDF eBook Book DescriptionAs data science and artificial intelligence (AI) become prevalent across industries, executives without formal education in statistics and machine learning, as well as data scientists moving into leadership roles, must learn how to make informed decisions about complex models and manage data teams. This book will elevate your leadership skills by guiding you through the core concepts of data science and AI. This comprehensive guide is designed to bridge the gap between business needs and technical solutions, empowering you to make informed decisions and drive measurable value within your organization. Through practical examples and clear explanations, you'll learn how to collect and analyze structured and unstructured data, build a strong foundation in statistics and machine learning, and evaluate models confidently. By recognizing common pitfalls and valuable use cases, you'll plan data science projects effectively, from the ground up to completion. Beyond technical aspects, this book provides tools to recruit top talent, manage high-performing teams, and stay up to date with industry advancements. By the end of this book, you’ll be able to characterize the data within your organization and frame business problems as data science problems.What you will learn Discover how to interpret common statistical quantities and make data-driven decisions Explore ML concepts as well as techniques in supervised, unsupervised, and reinforcement learning Find out how to evaluate statistical and machine learning models Understand the data science lifecycle, from development to monitoring of models in production Know when to use ML, statistical modeling, or traditional BI methods Manage data teams and data science projects effectively Who this book is for This book is designed for executives who want to understand and apply data science methods to enhance decision-making. It is also for individuals who work with or manage data scientists and machine learning engineers, such as chief data officers (CDOs), data science managers, and technical project managers.
