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Health care professionals often use regression methods to quantitatively describe the functional relationships between predictors and outcomes. However, little research investigates the appropriateness of tool application for response-surface-based design of experiments, the choice of regression estimators under different environments, and the impact of the determination of optimum conditions, or optimal factor-level settings, to achieve desirable target outcomes. Robust parameter design (RPD) is an established methodology for determining optimum conditions for a process to achieve specified process targets while minimizing variability of the outcomes. Underlying assumptions for RPD modeling and process conditions should be taken into account when selecting a regression estimator for developing fitted models. If these assumptions are incorrect, then a direct use of estimates obtained has the potential to be problematic, and the results may be potentially catastrophic, particularly when applied to the health care field. Many approaches to RPD in existing literature use ordinary least squares to obtain response functions by assuming normality and moderate variability in the underlying process. Given that some biologic processes are often highly variable and inherently asymmetric, a conditions-based approach for the selection of a regression estimate technique needs to be explored. This paper examines alternative approaches to regression estimation when the process data indicates that asymmetry or a high degree of process variability exists. The performance of select alternative regression methods is compared using Monte Carlo simulation and numerical analysis. [ABSTRACT FROM AUTHOR]

Quality practitioners often identify robust parameter design ( RPD) as one of the most important and effective methods for process and quality improvement. Within this framework, identifying the optimal factor settings that achieve desired process targets with minimum variance is critical and can translate to significant reductions in product waste and processing costs. In solving this problem, most traditional RPD models consider only a single quality characteristic of interest. However, products are often judged by multiple quality characteristics, which often have conflicting objectives. Conventional RPD models that address the multi-response problem typically only examine like-type cases, and those that consider mixed types of quality characteristics often overlook any asymmetry that is likely to exist in certain types. In contrast, this article proposes a multidisciplinary RPD methodology that provides an enhanced approach for modeling multiple, mixed type quality characteristics; uses the skew normal distribution to allow for a fuller and more accurate representation of asymmetric system properties and to facilitate simultaneous modeling of both symmetric and asymmetric conditions; and implements a priority-based optimization scheme that affords engineers' and decision makers' flexibility in establishing and modifying optimization priorities. A numerical example is used to demonstrate the proposed methodology, and the results are compared traditional approaches to illustrate potential improvements. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The primary goal of robust parameter design (RPD) is to determine the optimum operating conditions that achieve process performance targets while minimizing variability in the results. To achieve this goal, typical approaches to RPD problems use ordinary least squares methods to obtain response functions for the mean and variance by assuming that the experimental data follow a normal distribution and are relatively free of contaminants or outliers. Consequently, the most common estimators used in the initial tier of estimation are the sample mean and sample variance, as they are very good estimators when these assumptions hold. However, it is often the case that such assumed conditions do not exist in practice; notably, that inherent asymmetry pervades system outputs. If unaccounted for, such conditions can affect results tremendously by causing the quality of the estimates obtained using the sample mean and standard deviation to deteriorate. Focusing on asymmetric conditions, this paper examines several highly efficient estimators as alternatives to the sample mean and standard deviation. We then incorporate these estimators into RPD modeling and optimization approaches to ascertain which estimators tend to yield better solutions when skewness exists. Monte Carlo simulation and numerical studies are used to substantiate and compare the performance of the proposed methods with the traditional approach. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Continuous quality improvement is an effort to improve the quality of products, processes, or services. A program intended to effectively implement such efforts begins with the collection and analysis of data. The primary purpose of the normal probability plot, which is one of the most frequently used graphical tools by quality practitioners and researchers, is for normality testing; however, the plot offers other valuable insights into data analysis that have rarely been addressed in the research community. This article provides an overview of distributional characteristics in the context of the four sample moments and investigates how variations in these moments affect the normal probability plot, focusing primarily on the presence of skewness and kurtosis and the effects of variability. This article then lays out a comprehensive analysis of how various statistical characteristics within a data set can influence the shape and corresponding properties of a normal probability plot, demonstrating how variations in the characteristics of the data can reveal or mask the degree of concavity, convexity, or the S shape in the plot, as well as the spread of the data about the mean and in the tails. This can provide engineers with a better understanding of the ways in which data 'communicate' through the plot, thereby providing a better basis for initial assumptions, as well as facilitating more accurate model estimation and optimization results thereafter. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]