Dear Colleagues,

Given their key position in the process control industry, process monitoring techniques have been extensively investigated by industrial practitioners and academic control researchers. Experimental designs and statistical process control is one of the most popular data-based methods for process monitoring and is widely used in various industrial areas. Effective routines for process monitoring can help operators to run industrial processes efficiently at the same time as maintaining high product quality. Quality control and improvement is more than an engineering concern. Quality has become a major business strategy for increasing productivity and gaining competitive advantage. Statistical quality control is on the basis of the quality control chart, the use of mathematical statistics methods to make quality control quantitative, and scientific, effective prevention and control of process quality. Its main goal is to ensure that the characteristic value of product quality produced by all processes is equal to or close to the expected value as long as possible and to improve the process ability of the production process. Its main characteristic is to fully embody the process prevention principle of modern control theory. Statistical quality control refers to the use of statistical techniques for quality control. These techniques include the application of design of experiments, frequency distributions, major trends and discrete measurements, control charts, regression analysis, and significance testing.

Some of the related subjects that deserve to be studied more in depth are as follows:

• Sampling inspection;
• Multivariate statistical analysis;
• Theory of point estimation;
• Design of experiments;
• Control chart;
• Probability distribution;
• Hugh Hart control chart;
• Significance test;
• Asymptotic statistics;
• Testing statistical hypotheses;
• Statistical decision theory;
• Bayesian analysis.

Prof. Dr. Stelios D. Georgiou
Guest Editor

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• Statistical experimental designs
• Optimal designs
• Factorial designs
• Orthogonal arrays
• Saturated and supersaturated designs
• Weighing designs
• Latin hypercube designs
• Biostatistics
• Data analysis
• Combinatorial designs
• Response surface methodology
• Testing statistical hypotheses
• Statistical decision theory
• Bayesian analysis