• Journal of Korea Entertainment Industry Association
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• v.13 no.4
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• pp.401-410
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• 2019

This study was conducted to identify factors affecting nursing performance of delirium among nursing students. A total of 252 fourth year students were recruited from nursing department in Gwangju. Structured questionnaire was self-administrated from April to September, 2017. The used statistical analysis were t-test, ANOVA, Pearson's coefficient and multiple regression analysis. Knowledge of delirium 29.0±7.24, self-confidence in the care for delirium 71.65±28.55 and nursing performance level for patients with delirium was 41.16±8.97. Nursing performance of delirium had significant positive correlations with delirium knowledge, self-confidence of delirium care. In multiple regression analysis, nursing experience for delirium patients, self-confidence of delirium care, practice experience in intensive care unit, use of nursing diagnosis related to delirium, and satisfaction of clinical practice were significant factors of nursing performance of delirium explaining 29.8% of the variables. In conclusion, to enhance nursing performance of delirium, it is necessary to develop educational program for increasing nursing experience for delirium patients during clinical practice and self-confidence of delirium care.

• Journal of Korean Society on Water Environment
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• v.28 no.1
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• pp.38-49
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• 2012

In this study, we analyze statistical characteristics of influent water quality in Daejeon waste water treatment plant and apply a stochastic model for data generation. In the analysis, the influent water quality data from year 2003 to 2008, except for year 2006, are used. Among water quality variables, we find strong correlations between BOD and T-N; T-N and T-P; BOD and T-P; $COD_{Mn}$ and T-P; and BOD and $COD_{Mn}$. We also find that different water quality variables follow different theoretical probability distribution functions, which also depends on whether the seasonal cycle is removed. Finally, we generate the influent water quality data using the multi-season 1st Markov model (Thomas-Fiering model). With model parameters calibrated for the period 2003~2005, the generated data for 2007~2008 are well compared with observed data showing good agreement in general. BOD and T-N are underestimated by the stochastic model. This is mainly due to the statistical difference in observed data itself between two periods of 2003~2005 and 2007~2008. Therefore, we expect the stochastic model can be applied with more confidence in the case that the data follows stationary pattern.

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.697-708
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• 2008

Operational risk is defined as the risk of loss resulting from inadequate or failed internal processes, people and systems, or external events. The advanced measurement approach proposed by Basel committee uses loss distribution approach(LDA) which quantifies operational loss based on bank's own historical data and measurement system. LDA involves two distribution fittings(frequency and severity) and then generates aggregate loss distribution by employing mathematical convolution. An objective validation for the operational risk measurement is essential because the operational risk measurement allows flexibility and subjective judgement to calculate regulatory capital. However, the methodology to verify the soundness of the operational risk measurement was not fully developed because the internal operational loss data had been extremely sparse and the modeling of extreme tail was very difficult. In this paper, we propose a methodology for the validation of operational risk measurement based on bootstrap confidence intervals of operational VaR(value at risk). We derived two methods to generate confidence intervals of operational VaR.

• Journal of Distribution Science
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• v.10 no.11
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• pp.79-88
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• 2012

Purpose - The objective of this paper is to suggest that a company's CRM activities have an effect on customer loyalty in the Korean retail industry. Typically, Korean customers use large local marts with convenience in the absence of any other choice. Therefore, this study aims to shed light on the fact that customers do not break away from their preferred retail stores, either owing to their stringent loyalty (the lie loyalty) or difficulty in turning to alternative choices. Research design, data, methodology - By surveying a sample of 200 hyper-markets through a questionnaire, and excluding dubious and missing responses, I obtained 181 samples to be included in the empirical analysis. The survey was conducted for two weeks during October 2011. AMOS and SPSS18 statistical packages were used for conducting statistical analysis for this study. This paper was developed using the concept of customer equity on CRM, which is known to have a positive impact on customer loyalty through the satisfaction and switching-barrier parameters. The hypothesis of this paper is that customer equity is composed of relationship equity, value equity and brand equity, and that the relationship equity variable has positive effects on the value equity and brand equity amongst other types of customer equity. Moreover, customer equity influences customer loyalty through parameters including customer satisfaction and switching costs in the Korean retail industry. Results - According to the results of the analysis, it was confirmed that relationship value had a positive effect (+) on all variables, including the perceived QoS (Quality of Service), store brand images, economic value, and store convenience. It was also confirmed that the assumption that the perceived QoS (Quality of Service), economic value, and store convenience had a positive effect on customer satisfaction was shown to be statistically significant, with a p-value below 0.05. Only the store brand value variable had an effect on the switching-cost variable with respect to the causal sequence of the variables, including the perceived QoS, store brand value, economic value, and store convenience. The remaining variables did not seem to influence the switching-cost variable. On the other hand, another effect showed that customer satisfaction had a statistically significant influence on the switching-costs variable. Moreover, the customer satisfaction and switching-cost variables also had a statistical influence on customer loyalty. Conclusions - The CRM activities had an influence on various variables (including perceived QoS, perceived economic value, store brand value, and store convenience) pertaining to customer values. Customer satisfaction and switching-cost had some effects on customer loyalty as a parameter. This confirms that stringent loyalty exists with respect to customer loyalty in the retail industry. The fact that the variable had such a statistically significant influence on the switching-cost and store brand equity variables means that consumers react to the reputation of a brand, confidence about the store, and quality confidence. The implications of this study in the retail industry should be further extended to devise strategies for customer retention.

• Journal of the Korean Statistical Society
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• v.36 no.2
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• pp.201-208
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• 2007

Evolutionary operation (EVOP) proposed by Box (1957) is a method for continuous monitoring and improvement of a full-scale manufacturing process with the objective of moving the operating conditions toward the better ones. EVOP consists of systematically making small changes in the levels of the two or three process variables under consideration. Data are collected on the response variable at each point of two level factorial design with the center point and a cycle is said to have been completed. The cycles are replicated sequentially until the decision is made on whether further cycle of experiments is needed to conclude the significance of any of main effects or interaction effects or the curvature. In this paper, an improved flow chart of EVOP is proposed and how to determine the number of cycles is studied based on the size of type II error. In order to reject the alternative hypothesis of interests with more confidence and conclude that we believe in the null hypothesis of no effects, we propose a counter measure $p^*-value$ corresponding to the p-value. The relationship of $p^*-value$ to the probability of type II error ${\beta}$ under the alternative hypothesis of interests is analogous to that of p-value to the probability of type I error ${\alpha}$. Also the implementation of EVOP with a mixture experiment is discussed.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.485-494
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• 2015

Canonical correspondence analysis is an ordination method used to visualize the relationships among sites, species and environmental variables. However, projection results are fluctuations if the samples slightly change and consistent interpretation on ecological similarity among species tends to be difficult. We use the bootstrap methods for canonical correspondence analysis to solve this problem. The bootstrap method results show that the variations of coordinate points are inversely proportional to the number of observations and coverage rates with bootstrap confidence interval approximates to nominal probabilities.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.27-41
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• 2008

A graphical method of checking the adequacy of a generalized linear model is proposed. The graph helps to assess the assumption that the link function of mean can be expressed as a linear combination of explanatory variables in the generalized linear model. For the graph the boosting technique is applied to estimate nonparametrically the relationship between the link function of the mean and the explanatory variables, though any other nonparametric regression methods can be applied. Through simulation studies with normal and binary data, the effectiveness of the graph is demonstrated. And we list some limitations and technical details of the graph.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.1-11
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• 2018

Multivariate process control has become important in various applied fields. For instance, there are many situations in which the simultaneous monitoring of multivariate quality characteristics is necessary for the manufacturing industry. Despite its importance, its practical usage is not as convenient because it is difficult to identify the source of the out-of-control signal in a multivariate control chart. In this paper, we will introduce how to detect the source of the out-of-control by using confidence intervals for new observations, and will discuss the identification and interpretation of the out-of-control variable through simulation studies.

• The Mathematical Education
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• v.50 no.3
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• pp.263-284
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• 2011

We took note of the fact that there were not many studies on improvement of mathematics learning in the field of statistics for the minority students from the families who belonged to the Low-SES. This study was to help them understand the concepts and principles of mathematics, motivate them for mathematics learning, and have them feel familiar with it. The subjects were 12 students from the low-SES families among the sophomores of 00 High School in Gyeonggi-do. Although it could not be achieved effectively in the short-term of learning for the slow learners, their understanding of basic concepts and confidence, interests and concerns in statistical learning were remarkably changed, compared to their work in the beginning period. Our discourse classes using various topics and examples were well perceived by the students whose performance was improved up to the 3rd thinking level of Mooney's framework. Also, a meaningful instructional model for slow learners(IMSL) was found through the discourse.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.341-350
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• 2012

Composite quantile regression model is considered for iid error case. Since the regression coefficients are the same across different quantiles, composite quantile regression can be used to combine the strength across multiple quantile regression models. For the composite quantile regression, bootstrap method is examined for statistical inference including the selection of the number of quantiles and confidence intervals for the regression coefficients. Feasibility of the bootstrap method is demonstrated through a simulation study.