• Journal of Korean Society for Quality Management
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• v.7 no.2
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• pp.22-28
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• 1979

The problem considered in this paper is to select the vital factor effect to the product quality through the experimental design and analysis of response surface, so as to control the quality improvement of industrial product. In this time, even through the mathematical model is unknown it could be applicable to control the quality of industrial products and to determine optimum operating condition for many technical fields, particulary, for industrial manufacturing process. When a set of data is available from an experimental design, it is often of interest 1:0 fit polynominal repression model in independent variables (eg, time, temperature, pressure, etc) the optimize the response variable (eg. yield, strength etc). This paper proposes a method known to obtain the optimum operating condition, and how to find the condition by using table of orthogonal array experiments, and optimization methodology of statistical model. A criterion can be applied determining to optimum operating conditions in manufacturing industry and improving the fit of response surface which may be used for prediction of responses and quality control of industrial products.

• The Korean Journal of Applied Statistics
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• v.9 no.1
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• pp.75-89
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• 1996

This paper proposes a goodness-of-fit test for checking the adequacy of the additive risk model with a binary covariate. The test statistic is based on martingale residuals, which is the extended form of Wei(1984)'s test. The proposed test is shown to be consistent and asymptotically normally distributed under the regularity conditions. Furthermore, the test procedure is illustrated with two set of real data and the results are discussed.

• Journal of the Korean Society of Marine Environment & Safety
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• v.21 no.3
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• pp.253-258
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• 2015

The purpose of this study is to find suitable probability distribution function of complex distribution data like multimodal. Normal distribution is broadly used to assume probability distribution function. However, complex distribution data like multimodal are very hard to be estimated by using normal distribution function only, and there might be errors when other distribution functions including normal distribution function are used. In this study, we experimented to find fit probability distribution function in multimodal area, by using AIS(Automatic Identification System) observation data gathered in Mokpo port for a year of 2013. By using chi-squared statistic, gaussian mixture model(GMM) is the fittest model rather than other distribution functions, such as extreme value, generalized extreme value, logistic, and normal distribution. GMM was found to the fit model regard to multimodal data of maritime traffic flow distribution. Probability density function for collision probability and traffic flow distribution will be calculated much precisely in the future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.522-528
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• 2012

It is the aim of this paper to propose the empirical fatigue crack propagation model fit to describe a crack growth behavior of AZ31 magnesium alloys. The statistical data of a crack growth for an estimation are obtained by fatigue crack propagation tests under the three cases of maximum load. The empirical models estimated are Paris-Erdogan model, Walker model, Forman model, and modified-Forman model. It is found that the empirical model fit to describe a crack growth behavior of AZ31 magnesium alloys is Paris-Erdogan model and Walker model. It is also verified that a fatigue crack growth rate exponent of a empirical model is to be a material constant.

• Journal of Environmental Science International
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• v.19 no.10
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• pp.1229-1235
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• 2010

Three meteor-statistical forecasting models - the transfer function model, the time-series autoregressive model and the neural networks model - were tested to develop a daily forecasting model for Jejudo, where the need and demand for wind power forecasting has increased. All the meteorological observation sites in Jejudo have been classified into 6 groups using a cluster analysis. Four pairs of observation sites among them, all having strong wind speed correlation within the same meteorological group, were chosen for a model test. In the development of the wind speed forecasting model for Jejudo, it was confirmed that not only the use a wind dataset at the objective site itself, but the introduction of another wind dataset at the nearest site having a strong wind speed correlation within the same group, would enhance the goodness to fit of the forecasting. A transfer function model and a neural network model were also confirmed to offer reliable predictions, with the similar goodness to fit level.

• Journal of Korean Society of Transportation
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• v.26 no.1
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• pp.113-126
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• 2008

This research aims to explore the effects of multicollinearity on the reliability and goodness of fit of logit model. To investigate the effects of multicollinearity on the multinominal logit model, numerical experiments are performed. The exploratory variables(attributes of utility functions) which have a certain degree of correlations from (rho=) 0.0 to (rho=) 0.9 are generated and rho-squares and t-statistics which are the indices of goodness of fit and reliability of logit model are traced. From the well designed numerical experiments, following findings are validated : 1) When a new exploratory variable is added, some of rho-squares increase while the others decrease. 2) The higher relations between generic variables lead a logit model worse with respect to goodness of fit. 3) Multicollinearity has a tendency to produce over-evaluated parameters. 4) The reliability of the estimated parameter has a tendency to decrease when the correlations between attributes are high. These results suggest that we have to examine the existence of multicollinearity and perform the proper treatments to diminish multicollinearity when we develop logit model.

• Culinary science and hospitality research
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• v.16 no.5
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• pp.50-63
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• 2010

The purpose of this study is to investigate the effects of person-organization fit and person-job fit on occupational commitment, organizational commitment and turnover intentions among cooks in the foodservice industry. Data were collected by a total of 210 cooks at restaurants in the Seoul and Kyunggi area. The analysis of AMOS was used to test the causal model. The results indicate that the person-organization fit was positively related to occupational commitment and organizational commitment. The person-job fit was also found to correlate positively with occupational commitment and organizational commitment. Occupational commitment was positively related to turnover intentions while organizational commitment was negatively related to turnover intentions. Implications of the results and limitations of the study were discussed.

• The Journal of the Korea Contents Association
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• v.15 no.3
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• pp.290-302
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• 2015

The study purpose was to test the validity and reliability of the Korean version of the Revised Stress Appraisal Measure (RSAM) to assess stress appraisal in undergraduate students. Internal consistency reliability, construct and criterion validity were calculated using IBM SPSS Statistics 21 and AMOS 21 program. Survey data were collected from a convenience sample of 296 undergraduate students enrolled in five universities in G city and C area, South Korea. The Korean version of RSAM categorized into 5 factors explaining 68.4% of the total variance. The model of five subscales was validated by confirmatory factor analysis (p<.001, Goodness of Fit Index, Adjusted Goodness of Fit Index, Normed Fit Index, Comparative Fit Index >.08, Root Mean Square Error of Approximation=.056). In criterion validity, the scores for the scale were significantly correlated with the Perceived Stress Scale-Korean. Cronbach's alpha coefficient for the 19 items was .73~.89. The Korean RSAM showed satisfactory construct and criterion validity and reliability. Thus it may be an appropriate instrument for measuring stress appraisal in Korean university students.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.9
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• pp.4049-4054
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• 2014

Background: Race and ethnicity are significant factors in predicting survival time of breast cancer patients. In this study, we applied advanced statistical methods to predict the survival of White non-Hispanic female breast cancer patients, who were diagnosed between the years 1973 and 2009 in the United States (U.S.). Materials and Methods: Demographic data from the Surveillance Epidemiology and End Results (SEER) database were used for the purpose of this study. Nine states were randomly selected from 12 U.S. cancer registries. A stratified random sampling method was used to select 2,000 female breast cancer patients from these nine states. We compared four types of advanced statistical probability models to identify the best-fit model for the White non-Hispanic female breast cancer survival data. Three model building criterion were used to measure and compare goodness of fit of the models. These include Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC). In addition, we used a novel Bayesian method and the Markov Chain Monte Carlo technique to determine the posterior density function of the parameters. After evaluating the model parameters, we selected the model having the lowest DIC value. Using this Bayesian method, we derived the predictive survival density for future survival time and its related inferences. Results: The analytical sample of White non-Hispanic women included 2,000 breast cancer cases from the SEER database (1973-2009). The majority of cases were married (55.2%), the mean age of diagnosis was 63.61 years (SD = 14.24) and the mean survival time was 84 months (SD = 35.01). After comparing the four statistical models, results suggested that the exponentiated Weibull model (DIC= 19818.220) was a better fit for White non-Hispanic females' breast cancer survival data. This model predicted the survival times (in months) for White non-Hispanic women after implementation of precise estimates of the model parameters. Conclusions: By using modern model building criteria, we determined that the data best fit the exponentiated Weibull model. We incorporated precise estimates of the parameter into the predictive model and evaluated the survival inference for the White non-Hispanic female population. This method of analysis will assist researchers in making scientific and clinical conclusions when assessing survival time of breast cancer patients.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.682-693
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• 1995