About This Special Issue
Recently, statistical knowledge has become an important requirement and occupies a prominent position in the performance of various professions. Every day, professionals use statistical techniques increasingly sophisticated to aid in decision making.
The Statistical Engineering is a highly interdisciplinary area of knowledge focused on the design, analysis and optimization of experiments to solving real and complex multivariate problems with minimal costs involved in the trial. Moreover, it has been widely used in various fields like Engineering, Management, Logistics, Marketing, Tourism, Chemistry, Physics, Biology, among others.
The aim of this book is to bring together research papers, literature review and case studies with a multidisciplinary focus and organize them in a comprehensive way for the community interested in the field.
The advantage is that this Special Issue will cover a wide range of applications in diverse areas of knowledge for decision making based on sophisticated statistical tools that consider several variables at once. So, this Issue will not focus on just one area of application, for example, only in Chemistry or Economics as it happens traditionally. Thus, the representation of reality through statistical models become more realistic contributing to process improvement by reducing the variability, reprocessing, environmental impact. Thus, the processes are better monitored and controlled with greater profits.
Topics covered, but not limited to:
Design of Experiments
Full Factorial Design
Fractional Factorial Design
Optimization Methods (Response Surface Method, Desirability, among others)
Multivariate Analysis and Data Mining
Principal Component Analysis (PCA)
Principal Component Regression (PCR)
Partial Least Squares Regression (PLS)
Support Vector Machine Regression (SVMR)
Artificial Neural Networks (ANN)
Statistical Process Control (SPC)
Measurement System Analysis (MSA)
Applications, but not limited to:
Chemistry (Advanced Oxidation Process-AOP, among others)