1. Statistical principles in quality—more basic definitions 2. Normal curve, variation and standard deviation as foundations, six sigma 3. Attribute data discussion beginning 4. Additional perspective: attributes and check sheets as pre-control 5. P chart steps for attribute data 6. Attributes, checklists and charting, and more for lean, service 7. Quality characteristics, accurate data and var...

Monitoring multi-attribute processes is an important issue in many quality control environments. Almost all the priory proposed control charts utilize equal weights for each Attribute Quality Characteristics (AQCs). In such condition, there is no priority among AQCs. But in real-world, compensatory may exist. Hence due to some applied reasons such as function or efficiency, unequal weights for ...

This paper is about how to control the quality of microarray expression data. Since gene-expression microarrays have become almost as widely used as measurement tools in biological research, we survey microarray experimental data to see possibilities and problems to control microarray expression data. We use both variable measure and attribute measure to visualize microarray expression data. Ac...

In some statistical process control applications, the quality of the product is characterized by the combination of both correlated variable and attributes quality characteristics. In this paper, we propose a novel control scheme based on the combination of two multi-layer perceptron neural networks for simultaneous monitoring of mean vector as well as the covariance matrix in multivariate-attr...