Contemporary Experimental Design Multivariate Analysis And Data Mining

The collection and analysis of data play an important role in many fields of science and technology, such as computational biology, quantitative finance, information engineering, machine learning, neuroscience, medicine, and the social sciences. Especially in the era of big data, researchers can easily collect data characterised by massive dimensions and complexity. In celebration of Professor Kai-Tai Fang’s 80th birthday, we present this book, which furthers new and exciting developments in modern statistical theories, methods and applications. The book features four review papers on Professor Fang’s numerous contributions to the fields of experimental design, multivariate analysis, data mining and education. It also contains twenty research articles contributed by prominent and active figures in their fields. The articles cover a wide range of important topics such as experimental design, multivariate analysis, data mining, hypothesis testing and statistical models.

This book furthers new and exciting developments in experimental designs, multivariate analysis, biostatistics, model selection and related subjects. It features articles contributed by many prominent and active figures in their fields. These articles cover a wide array of important issues in modern statistical theory, methods and their applications. Distinctive features of the collections of articles are their coherence and advance in knowledge discoveries.

"Multivariate Data Analysis - in practice adopts a practical, non-mathematical approach to multivariate data analysis. The book's principal objective is to provide a conceptual framework for multivariate data analysis techniques, enabling the reader to apply these in his or her own field. Features: Focuses on the practical application of multivariate techniques such as PCA, PCR and PLS and experimental design. Non-mathematical approach - ideal for analysts with little or no background in statistics. Step by step introduction of new concepts and techniques promotes ease of learning. Theory supported by hands-on exercises based on real-world data. A full training copy of The Unscrambler (for Windows 95, Windows NT 3.51 or later versions) including data sets for the exercises is available. Tutorial exercises based on data from real-world applications are used throughout the book to illustrate the use of the techniques introduced, providing the reader with a working knowledge of modern multivariate data analysis and experimental design. All exercises use The Unscrambler, a de facto industry standard for multivariate data analysis software packages. Multivariate Data Analysis in Practice is an excellent self-study text for scientists, chemists and engineers from all disciplines (non-statisticians) wishing to exploit the power of practical multivariate methods. It is very suitable for teaching purposes at the introductory level, and it can always be supplemented with higher level theoretical literature."Résumé de l'éditeur.

In the realm of psychological and brain sciences, there is a growing urgency to refine individual performance using personalized interventions that account for unique cognitive and biological attributes. Yet, the quest for such tailored approaches has proven challenging, as conventional methods often fall short. The limited integration of domain expertise and human judgment curtails the potential of artificial intelligence (AI) in effectively optimizing human performance, particularly in areas like customized training, health monitoring, and cognitive enhancement. Bridging the gap between AI capabilities and the specific requirements of individuals becomes crucial in meeting this rising demand. Advances in Artificial and Human Intelligence in the Modern Era present a transformative solution to tackle the prevailing challenges at the intersection of AI and human performance enhancement. This book delves deeply into the latest empirical research, literature reviews, and methodological advancements to introduce precision AI techniques for personalized interventions. By examining how the amalgamation of domain expertise and human insights can enhance AI performance, the book establishes a comprehensive framework for modeling individual distinctions and devising effective, tailored AI approaches. Tailored for academic scholars and researchers in psychological and brain sciences, computer science, and related fields, this book provides a comprehensive exploration of pioneering advancements in the convergence of artificial and human intelligence. Its diverse chapters encompass a wide array of topics, including the identification of mental health concerns, integration of human intelligence into AI tools, enhancement of reliability, and exploration of data standards. As it fuses expertise from these two disciplines, the book paves the way for a new era of personalized interventions with the potential to revolutionize human cognitive enhancement, training, and overall well-being.

The second edition of this book illuminates the fundamental principle and applications of probability-based multi-objective optimization for material selection in viewpoint of system theory, in which a brand new concept of preferable probability and its assessment as well as other treatments are introduced by authors for the first time. Hybrids of the new approach with experimental design methodologies (response surface methodology, orthogonal experimental design, and uniform experimental design) are all performed; robustness assessment and performance utility with desirable value are included; discretization treatment in the evaluation is presented; fuzzy-based approach and cluster analysis are involved; applications in portfolio investment and shortest path problem are concerned as well. The authors wish this work will cast a brick to attract jade and would make its contributions to relevant fields as a paving stone. It is designed to be used as a textbook for postgraduate and advanced undergraduate students in relevant majors, while also serving as a valuable reference book for scientists and engineers involved in related fields.

Experiments in the field and in the laboratory cannot avoid random error and statistical methods are essential for their efficient design and analysis. Authored by leading experts in key fields, this text provides many examples of SAS code, results, plots and tables, along with a fully supported website.