• Journal of the military operations research society of Korea
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• v.27 no.1
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• pp.101-113
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• 2001

Taguchi and VTA(variation Transmission Analysis) methods have been widely used recently as new methods for product design. In this study, Taguchi method using analysis of variance and VTA method using regression analysis are reviewed and compared with each other in terms of parameter design and tolerance design. In analysis of variance, variation of quality characteristics arises from noise factors, therefore the optimal levels of design factors are selected to minimize the effect of noise factors. n regression analysis, variation of quality characteristics arises from variation of each own design factors. As a method to reduce variation of these quality characteristics, sensitivity analysis was performed for each design factors. An example of calculating tolerance interval for the given defect rate in PPM is also introduced. Especially, the new method is suggested to increase the estimation accuracy of variation of quality characteristics through regression analysis.

• Journal of the Korean Society of Clothing and Textiles
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• v.20 no.2
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• pp.373-389
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• 1996

The purpose of this study was to classify the somatotype based on the side view and to analyze the characteristics of each somatotype. The subjects were 201 middle-aged women aged from 35 to 54. Data were collected through anthropometry and photometry and analyzed by factor analysis, cluster analysis, analysis of variance, and discriminant analysis. As the result of factor analysis for the classification of somatotypes, 6 factors which explain 80.8% of variance were extracted from 35 photometric measurement. Using factor scores cluster analysis was carried out and the subjects were classified into 4 cluster Each cluster was classified as straight type, turning over type, bending type and swayback according to its position to the relative plumb line and their side view contour. And 4 somatotypes were analyzed by theirs direct anthropometric and indirect Photometric measurment to represent physical characteristics of each group.

• International Journal of Concrete Structures and Materials
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• v.18 no.2E
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• pp.89-95
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• 2006

This paper proposes a modified method of Latin Hypercube sampling to reduce the variance of statistical parameters in uncertainty analysis of concrete structures. The proposed method is a modification of Latin Hypercube sampling method. This analysis method uses specifically modified tables of random permutations of ranked numbers. In addition, the Spearman coefficient is used to make modified tables. Numerical analysis is carried out to predict the uncertainty of axial shortening in prestressed concrete bridge. Statistical parameters obtained from modified Latin Hypercube sampling method and conventional Latin Hypercube sampling method are compared and evaluated by a numeric analysis. The results show that the proposed method results in a decrease in the variance of statistical parameters. This indicates the method is efficient and effective in the uncertainty analysis of complex structural system such as prestressed concrete bridges.

• Journal of Korean Society for Quality Management
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• v.25 no.2
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• pp.140-153
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• 1997

As product quality is maily determined in the product design and process design step, systematic design should be performed through parameter design and tolerance design. Therefore, we introduced analysis of variance and regression analysis as a statistical method which determine optimal levels of affective design factors to product characteristics, then we compared that process and result. In analysis of variance, variation of quality characteristics arises from noise factors, so the optimal levels of design factors are selected to minimize the effect of noise factors. In regression analysis, variation of quality characteristics aries from variation of each own design factors. As a method to reduce variation of these quality characteristics, sensitivity analysis was performed about each design factors. Through this sensitivity analysis, we represented process to calculate the interaction term of the factors.

• Genomics & Informatics
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• v.20 no.1
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• pp.9.1-9.6
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• 2022

Mendelian randomization (MR) uses genetic variation as a natural experiment to investigate the causal effects of modifiable risk factors (exposures) on outcomes. Two-sample Mendelian randomization (2SMR) is widely used to measure causal effects between exposures and outcomes via genome-wide association studies. 2SMR can increase statistical power by utilizing summary statistics from large consortia such as the UK Biobank. However, the first-order term approximation of standard error is commonly used when applying 2SMR. This approximation can underestimate the variance of causal effects in MR, which can lead to an increased false-positive rate. An alternative is to use the second-order approximation of the standard error, which can considerably correct for the deviation of the first-order approximation. In this study, we simulated MR to show the degree to which the first-order approximation underestimates the variance. We show that depending on the specific situation, the first-order approximation can underestimate the variance almost by half when compared to the true variance, whereas the second-order approximation is robust and accurate.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1628-1633
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• 2016

This paper derives an effective shape of the ferromagnetic pole pieces (low-speed rotor) for the reduction of transmission torque ripple in a magnetic-geared machine based on a Box-Behnken design (BBD). In particular, using a non-linear finite element method (FEM) based on 2-D numerical analysis, we conduct a numerical investigation and analysis between independent variables (selected by the BBD) and reaction variables. In addition, we derive a regression equation for reaction variables according to the independent variables by using multiple regression analysis and analysis of variance (ANOVA). We assess the validity of the optimized design by comparing characteristics of the optimized model derived from a response surface analysis and an initial model.

• Journal of the Korean Society of Food Culture
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• v.3 no.1
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• pp.57-66
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• 1988

The results obtained by survey on 599 respondents of different age groups for preference of 84 prepared foods were subjected to statistical principal component analysis, factor analysis, analysis of variance, scheffe verification, and discriminant analysis so as to find a structure of preference for foods. The results may be summarized as follows: 1. The results of the factor analysis indicate that 84 prepared food items may be classified into 3 groups and that by knowing an indvidual's preference to one prepared food, one can presume his preference to the others in the same group without carrying out actual test. 2. The results from an analysis of variance showed that most of primary school children extremely liked snacks, seniors liked Korean cooking, primary school children and collegians had weaknesses for western cookings while kindergarden children and adults over 50 years old disliked them.

• Journal of the Korean Society of Clothing and Textiles
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• v.21 no.7
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• pp.1227-1235
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• 1997

The purpose of this study was to classify the somatotype based on the side view and to analyze the characteristics of each somatotype. In order to reduce the burden of stocks and increase clothing fitness, systematic information on typical body sizes and somatotypes is essential. The subjects were 206 unmarried women aged from 19-29. Data were collected through anthropometry and photometry and analyzed by factor analysis, cluster analysis and analysis of variance. As the result of factor analysis for the classification of somatotypes, 8 factors which explain 74.7% of variance were extracted from 35 photometric and 17 anthrometric data. Using factor scores cluster analysis was carried out and the subjects were classified into 4 cluster.Each cluster was classified as bending type, swayback, turning over type and straight type accordding to its position to the relativeplumb line and their side view contour.

• The Journal of the Korea Contents Association
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• v.5 no.5
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• pp.266-272
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• 2005

This study is to empirically analyze the effects on job performance of Accounting Information Systems, Analysis methods were used to Cronbach's Alpha analysis, Factor analysis, analysis of variance(ANOVA) and regression in odor by the contingency grouping method. The results of this study are as follows : First, The regression analysis had effects on AIS utility and job performance. Second, The Analysis of variance(ANOVA) had non-effects on systems operating degree and systems satisfaction. Third, The input variables of information accuracy and systems satisfaction had additional effect about IT capability.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.163-178
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• 2023

GARCH-X(1, 1) model specifies that conditional variance follows an AR(1) process and includes a past exogenous variable. This study proposes a new class from that model by allowing a more general (non-linear) variance function to follow an AR(1) process. The functions applied to the variance equation include exponential, Tukey's ladder, and Yeo-Johnson transformations. In the framework of normal and student-t distributions for return errors, the empirical analysis focuses on two stock indices data in developed countries (FTSE100 and SP500) over the daily period from January 2000 to December 2020. This study uses 10-minute realized volatility as the exogenous component. The parameters of considered models are estimated using the adaptive random walk metropolis method in the Monte Carlo Markov chain algorithm and implemented in the Matlab program. The 95% highest posterior density intervals show that the three transformations are significant for the GARCHX(1, 1) model. In general, based on the Akaike information criterion, the GARCH-X(1, 1) model that has return errors with student-t distribution and variance transformed by Tukey's ladder function provides the best data fit. In forecasting value-at-risk with the 95% confidence level, the Christoffersen's independence test suggest that non-linear models is the most suitable for modeling return data, especially model with the Tukey's ladder transformation.