Integrative Approaches to Quality, Data Analysis, and Interdisciplinary Research

Offers methods for data analysis and interdisciplinary research, including statistical methods, CUSUM control charts, Poisson models, Bayesian approach, software programs, ML algorithms, big data, and more for addressing complex challenges in areas including environmental problems, healthcare, agriculture, and more.

Offering a wide range of case studies, this new book highlights the practical applications of integrating quality management, data analysis, and multidisciplinary research for solving urgent environmental challenges, improving public health and company operations, and more. By bridging the gap between theory and practice, it emphasizes the transformative power of data and interdisciplinary approaches in addressing complex challenges and driving innovation. This book highlights many methods and applications for data analysis and interdisciplinary research, including various statistical methods, CUSUM (Cumulative Sum) control charts, Poisson distribution models, the Bayesian approach, software programs like Mathematica, machine learning algorithms, big data, and more. The book applies data analysis approaches in addressing complex challenges in many areas, including for environmental problems such as measuring air pollution; in socioeconomic issues like insurance costs and dynamics; in healthcare such as for analyzing epidemiological data (such as from the COVID-19 pandemic); in the agricultural sector, such as to understand and forecast agricultural trends and to enhance agricultural sustainability; and more.