With the advent of lean-six sigma era, an extensive use of analytic tools such as control charts is required in the field of manufacturing. In relation to statistical quality control (SQC) or process control (SPC), the Korean standards have undergone a meaningful change. In this study, the theoretic backgrounds for evaluating the control limits in connection with the variable control charts are examined in view of better understanding the related constants and coefficients. This paper is intended to help the quality control practitioners understand the mathematical backgrounds by comparing related quality control constants and also to encourage them to make use of and to take the advantage of the variable control charts which are very useful for implementing the concept of lean-six sigma in many industrial sites.

In this paper, we have considered the modeling and analyses of categorical data. We modeled binary data with categorical predictors, using logistic regression to develop a statistical method. We found that ANOVA-type analyses often performed unsatisfactory, even when using arcsine-square-root transformations. We concluded that such methods are not appropriate, especially in cases where the fractions were close to 0 or 1. The logistic transformation of fraction data could be a promising alternative, but it is not desirable in the statistical sense. The major purpose of this paper is to demonstrate that logistic regression with an ANOVA-model like parameterization aids our understanding and provides a somewhat different, but sound, statistical background. We examined a simple real-world example to show that we can efficiently test the significance of regression parameters, look for interactions, estimate confidence intervals, and calculate the difference between the mean values of the referent and experimental subgroups. This paper demonstrates that precise confidence interval estimates can be obtained using the proposed ANOVA-model like approach. The method discussed here can be extended to any type of fraction data analysis, particularly for experimental design.

An estimation of indices for potential process capability (Cpk) and/or overall process performance (Ppk) is considered in order to gain insight into the statistical process control. The similarity and the difference of the two indices are discussed in some detail to clarify the meaning and usage of the two indices. It is demonstrated that the short term variance can be estimated within the framework of analysis of variance (ANOVA). Theoretic background is examined and followed by a simple numerical example, with a view to the implementation of the concept in the industrial fields.

When it comes to designing a product or a process, the robust parameter design (RPD) methodology devised by Taguchi is recognized as a way to produce products or processes with less variability, while the specifications of products or processes are met, so that the ratio of nonconforming products can be as minimized as possible. Nevertheless, as a matter of fact, there have been many pros and cons concerning the RPD method, mainly because of the use of signal to noise ratio as the measure of quality characteristic. Meanwhile, the variability analysis method has been highly recommended as an alternative of the RPD in the literature. In this paper, it is demonstrated that RPD can also be implemented within the framework of the variability analysis, which is sounder in the statistical sense. In light of an example of RPD approach, the modeling and estimation procedure is discussed in some detail with a view to a comparison of the two methods.

An attempt is given to the problem of analyzing the two-way binary attribute data using the logistic regression model in order to find a sound statistical methodology. It is demonstrated that the analysis of variance (ANOVA) may not be good enough, especi

In this paper, an evaluation of a product replacement ratio of irreparable items to the normally working ones is performed with a view to a warranty analysis. It is demonstrated that the replacement ratio during the warranty period can be estimated from t

In this paper, an attempt is given to the problem of short-term forecasting of domestic demand for mineral water in Korean market. The seasonal regression models are taken into consideration in model building and in making the forecast. As it turned out, the model performance is satisfactory for the use of production planning. A modeling procedure as well as a few fitted models are presented in some detail in order to discuss on the modeling procedure and forecasting performance.

In this paper, an analysis of two-way binary attribute data is performed using the logistic regression model in order to find a sound statistical methodology. It is demonstrated that the ANOVA may not be enough, especially for the case that the proportion is very low or high. The logistic transformation of proportion data could be a help, but not sound in the statistical sense. The adoption of generalized least squares(GLS) method entails much to estimate the variance-covariance matrix. On the other hand, the logistic regression methodology provides sound statistical background in estimating model parameters and related confidence intervals. The efficiencies of estimates are ensured with a simulated data with a view to demonstrate the usefulness of the methodology.