• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.443-452
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• 2001

The 2x3 cross-over design is proposed for the bioequivalence of two test drug formulations with a reference drug formulation. Oh et al.(1999) and Park et al.(1998) derived 3x2 cross-over design and discussed its benefits, since the 3x3 cross-over design may not be of practical design. We discuss the statistical issues for2x3 cross-over design and show its statistical properties. Bioequivalence problem in 2x3 cross-over design is considered statistically and an illustrated example is given.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.12
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• pp.1449-1454
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• 2009

The performance variation of a multibody system is affected by a variation of various design variables of the system. And the effects of design variable variations on the performance variation must be considered in design of a multibody system. Accordingly, a variation analysis of a multibody system needs to be conducted in design of a multibody system. For a variation analysis of a performance, population mean and variance which are called statistical parameters of design variables are needed. However, an evaluation of statistical parameters of design variables is impossible in many practical cases. Therefore, an estimation of statistical parameters of the performance based on sample mean and variance which are called statistic of design variables is needed. In this paper, the variation analysis method for a multibody system based on design variable samples was proposed. And, using the proposed method, a variation analysis of the vehicle ride comfort based on sample statistic of design variables was conducted.

• Journal of Korean Society for Quality Management
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• v.32 no.1
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• pp.92-101
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• 2004

Tris paper present an economic statistical design which have statistically constraints for the optimal design of an EWMA control charts with variable sampling interval. Cost function use that proposed by Lorenzen and Vance, and the optimal design parameters include the sample size, control limit width, sampling interval, EWMA weight value. Comparisons between VSI EWMA control charts optimal economic design and optimal economic statistical designs show the following fact. Although have demerits which are more costly than economic design, have merits which to detect shifts more efficiently and to improve statistical performance.

• Communications for Statistical Applications and Methods
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• v.9 no.1
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• pp.167-174
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• 2002

Computer network provides a powerful infrastructure for information sharing and the development of the statistical software with new concepts. In this paper, we discuss the design concepts of the statistical software in the network environment.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.61-69
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• 2005

In conducting the analysis of variance for the split-plot design using the statistical package SPSS, users including statisticians are faced with difficulties because of no appropriate example in the SPSS applications guide book. In this paper, therefore, we present an analysis of variance procedure for the split-plot design using SPSS syntax window.

• Journal of Preventive Medicine and Public Health
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• v.46 no.2
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• pp.96-104
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• 2013

Objectives: The inherent nature of the Korean National Health and Nutrition Examination Survey (KNHANES) design requires special analysis by incorporating sample weights, stratification, and clustering not used in ordinary statistical procedures. Methods: This study investigated the proportion of research papers that have used an appropriate statistical methodology out of the research papers analyzing the KNHANES cited in the PubMed online system from 2007 to 2012. We also compared differences in mean and regression estimates between the ordinary statistical data analyses without sampling weight and design-based data analyses using the KNHANES 2008 to 2010. Results: Of the 247 research articles cited in PubMed, only 19.8% of all articles used survey design analysis, compared with 80.2% of articles that used ordinary statistical analysis, treating KNHANES data as if it were collected using a simple random sampling method. Means and standard errors differed between the ordinary statistical data analyses and design-based analyses, and the standard errors in the design-based analyses tended to be larger than those in the ordinary statistical data analyses. Conclusions: Ignoring complex survey design can result in biased estimates and overstated significance levels. Sample weights, stratification, and clustering of the design must be incorporated into analyses to ensure the development of appropriate estimates and standard errors of these estimates.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.137-142
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• 2003

We discuss why sample design by mathematical programming can be beneficial to practical surveys. We illustrate some developments of software for sample design using mathematical programming in several statistical organizations. Also, we present certain restrictions on the use of mathematical programming.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.243-248
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• 1995

The problem of optimal design for a nonparametric regression with binary data is considered. The aim of the statistical analysis is the estimation of a quantal response surface in two dimensions. Bias, variance and IMSE of kernel estimates are derived. The optimal design density with respect to asymptotic IMSE is constructed.

• Korean Management Science Review
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• v.13 no.2
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• pp.61-71
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• 1996

In this paper, we propose an interface design for helping users to utilize a statistical analysis program efficiently. This interface design is useful to suggest which statistical method to use and to provide the result by linking data file to the statistical analysis program in a user friendly way. In chapter 2, we explain the overall structure and modules of the interface.

• Journal of the Korean Statistical Society
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• v.31 no.3
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• pp.277-299
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• 2002

Most statistical designs, such as orthogonal designs and optimal designs, are based on a specific statistical model. It is very often that the experimenter does not completely know the underlying model between the response and the factors. In computer experiments, the underlying model is known, but too complicated. In this case we can treat the model as a black box, or model to be unknown. Both cases need a space filling design. The uniform design is one of space filling designs and seeks experimental points to be uniformly scattered on the domain. The uniform design can be used for computer experiments and also for industrial experiments when the underlying model is unknown. In this paper we shall introduce the theory and method of the uniform design and related data analysis and modelling methods. Applications of the uniform design to industry and other areas are discussed.