• Journal of the Korean Society of Costume
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• v.53 no.7
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• pp.29-36
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• 2003

The purposes of this syudy were to identify the factors of perceived risk, satisfaction, and intention on rental clothing. The subjects were 767 adults residing in Jeonbuk province. The research was made from February 20 to March 19, 2003. Fof data analysis. frequencies, percentages, mean, and standard deviations were calculated. Also, factor analysis and stepwise multiple-regression analysis were done. The results were as follows: 1. Approximately half of the respondents had experiences of rental clothing, and the most frequent items were wedding dress, degree gown, performance dress, and sports wear in descending order. 2. The factors of perceived risk of rental clothing were divided into lost, appropriateness, and damage, and total variance was 62.51%. The perceived risk about damage of rental clothing was the highest, and lost was the lowest. 3. The factors of rental clothing satisfaction were divided into appearances, store status, function, and fitness, and total variance was 59.96%. The satisfaction of function of rental clothing was the highest, and the store status was the lowest. 4. The intention on rental clothing was high in case of higher interest in rental clothing, higher educational level. more experiences in rental clothing, lower perceived risk of damage on the rental clothing, and younger person, and these variables explained 54.00% of the intention on rental clothing.

• Child Health Nursing Research
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• v.9 no.3
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• pp.323-331
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• 2003

Purpose: The purpose of this study is to analyze the factors affecting Deviant Behaviors of Korean High school Students. Method: Data was collected from October 8 to 31, 2002. The subjects for this study were 697 Korean High school Students(boys 347, girls 350), recruited from two High School located in Seoul. Data collection was conducted through the use of 6 Questionnaire that modified by the investigator. The data was analyzed by the SPSS win 10.0 program using Descriptive statistics, Pearson Correlation coefficient and stepwise multiple regression. Result: 1) The mean of total item score the Deviant Behaviors scales was 1.59, which was slightly low. 2) There was a significant correlation between Deviant Behaviors, Type A Personality, Aggression, Impulsivity, Stress and Social Support(γ= .11 ～ .65, p<.001), but It was no significant correlation Type A Personality and Stress(γ= -.01). 3) Stepwise multiple regression analysis showed that (1) Impulsivity, Social Support and Type A Personality were the predictors of Deviant Behaviors and account for 18.6％ of the variance in Deviant Behaviors. (2) Impulsivity account for 3.6％ of the variance in Deviant Behaviors in Subjects with a lower degree of score the Deviant Behaviors. Impulsivity and Social Support account for 23.2％ of the variance in Deviant Behaviors in subjects with higher degree of score the Deviant Behaviors. (3) Impulsivity account for 18.3％ of the variance in Deviant Behaviors in High school girls (n=350). Impulsivity and Social Support account for 20.1％ of the variance in Deviant Behaviors in High school boys (n=347). Conclusion: Impulsivity and Social Support account for Deviant Behaviors of High school Students. Therefore it is necessary to develop nursing intervention to reduce the level of Impulsivity, to increase the Social Support in order to decrease the Deviant Behaviors.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.5
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• pp.697-703
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• 2018

In this paper, we analyzed the operation of the ignition circuit for electronic arm and fire device(EAFD), and investigated the sensitive elements of the circuit system. For reliability analysis, the EAFD ignition circuit was modeled using the PSpice simulation tool, and the output results of the circuit were examined by changing the tolerance of each circuit element. Monte Carlo simulation was used by maintaining the values of the observed sensitive elements at ${\pm}10%$ of the original values and adjusting the values of the other components according to a random distribution. The histogram results of the output peak currents and pulse widths were represented by Weibull and Burr type XII function fittings in three cases(element values are +10 %, 0 %, -10 % of original). For the output peak currents, mean values were 1.0028, 1.0034, and 1.0050, where the variance values were calculated as 0.0398, 0.0396, and 0.0290 using the Weibull function fitting, respectively. For pulse widths, the mean values of 0.9475, 0.9907, and 1.0293 with the variance values of 0.0260, 0.0251, and 0.0238 were obtained using the Burr Type XII function fittings.

• 한국해양학회지
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• v.24 no.1
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• pp.1-14
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• 1989

This study analyzes the interannual periodicity by using the statistical techniques of probability, spectral analysis, empirical orthogonal function analysis (EOF), and coherency analysis. The data base for this study is the time series of 1971-1985 temperature, salinity in the southern waters of the East Sea, 1960-1986 mean sea level at Pusan and Izuhara, and 1960-1986 sea level atmospheric pressure at Pusan. The appearances of anomalous temperatures higher and lower than 15-year mean monthly average with one standard deviation are about 30% of total data. The significant interannual period for temperature, salinity and sea level fluctuation is 36.6, and 23.3 months. The empirical orthogonal function analyses show that the 1st mode of the EOFs is responsible for more than 90% of total variance of the surface temperature variations, while in near-bottom waters, the relative importance of the higher EOF modes is much greater explaining more than 30% of total variance. The coherency between normalized temperatures and salinities is significant at the interannual period of 36.6 and 21.3 months.

• The Korean Journal of Applied Statistics
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• v.10 no.1
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• pp.69-84
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• 1997

We consider a linear calibration problem, $y_i = $$\alpha + \beta (x_i - x_0) + \epsilon_i$, $i=1, 2, {\cdot}{\cdot},n$ $y_f = \alpha + \beta (x_f - x_0) + \epsilon, $ where we observe $(x_i, y_i)$'s for the controlled calibration experiments and later we make inference about $x_f$ from a new observation $y_f$. The objective of the calibration design problem is to find the optimal design $x = (x_i, \cdots, x_n$ that gives the best estimates for $x_f$. We compare Kim(1989)'s Bayesian design which minimizes the expected value of the posterior variance of $x_f$ and some optimal designs from literature. Kim suggested the Bayesian optimal design based on the analysis of the characteristics of the expected loss function and numerical must be equal to the prior mean and that the sum of squares be as large as possible. The designs to be compared are (1) Buonaccorsi(1986)'s AV optimal design that minimizes the average asymptotic variance of the classical estimators, (2) D-optimal and A-optimal design for the linear regression model that optimize some functions of $M(x) = \sum x_i x_i'$, and (3) Hunter & Lamboy (1981)'s reference design from their paper. In order to compare the designs which are optimal in some sense, we consider two criteria. First, we compare them by the expected posterior variance criterion and secondly, we perform the Monte Carlo simulation to obtain the HPD intervals and compare the lengths of them. If the prior mean of $x_f$ is at the center of the finite design interval, then the Bayesian, AV optimal, D-optimal and A-optimal designs are indentical and they are equally weighted end-point design. However if the prior mean is not at the center, then they are not expected to be identical.In this case, we demonstrate that the almost Bayesian-optimal design was slightly better than the approximate AV optimal design. We also investigate the effects of the prior variance of the parameters and solution for the case when the number of experiments is odd.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.327-336
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• 2001

This work concerns estimating a regression function, which is not linear, using aggregate data. In much of the empirical research, data are aggregated for various reasons before statistical analysis. In a traditional parametric approach, a linear estimation of the non-linear function with aggregate data can result in unstable estimators of the parameters. More serious consequence is the bias in the estimation of the non-linear function. The approach we employ is the kernel regression smoothing. We describe the conditions when the aggregate data can be used to estimate the regression function efficiently. Numerical examples will illustrate our findings.

• Proceedings of the Safety Management and Science Conference
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• 2009.11a
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• pp.557-563
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• 2009

The research develops measurement processes for estimating and evaluating the gauge R&R(Reproducibility & Repeatability) using ANOVA(Analysis of Variance) of experimental design tools. The ten-step processes developed include experimental goal setting, the selection of characteristics(factors, levels), data model, ANOVA, EMS(Expected Mean Square), estimation of gauge precisions, and evaluation indexes. The three-factor combined measurement models are presented to show the processes developed in this paper.

• Women's Health Nursing
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• v.27 no.4
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• pp.358-367
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• 2021

Purpose: This study aimed to identify factors associated with diabetes management self-efficacy in pregnant women with gestational diabetes mellitus (GDM) in Korea. Methods: A total of 173 pregnant women with GDM in Korea were recruited by posting announcements at two Korean online communities focusing on pregnancy and GDM. Participants completed a structured online survey from July to September 2018. Thirteen inappropriate responses were excluded and a total of 160 questionnaires were used in the final analysis. Descriptive statistics were calculated and multiple regression with the enter method was done to identify the associations of depressive mood, anxiety, emotional intelligence, and sleep quality with diabetes management self-efficacy. Results: Respondents reported a moderately depressive mood (mean, 10.36), low to moderate anxiety (mean, 41.65), above-average emotional intelligence (mean, 78.04), moderate sleep quality (mean, 42.01), and above-average diabetes management self-efficacy (mean, 52.29). The major factor associated with diabetes management self-efficacy of pregnant women with GDM was emotional intelligence (β=.51, p<.001). Other factors, in descending order of influence, were sleep quality (β=.22, p<.001) and exercise (β=.18, p=.004). Taken together, the aforementioned factors explained 34.6% (F=39.53, p<.001) of the total variance. Conclusion: The results of this study suggest that to improve the diabetes management self-efficacy of pregnant women with GDM, it is necessary to develop an education program that can also enhance emotional intelligence, sleep quality, and exercise.

• Journal of Korean Clinical Nursing Research
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• v.23 no.3
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• pp.332-340
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• 2017

Purpose: This study was to identify the quality of life and its related factors in patients with benign prostatic hyperplasia. Methods: A cross-sectional survey design was utilized. Data were collected using questionnaires from 128 patients with benign prostatic hyperplasia who visited an outpatient department at one general hospital in 2016. Data were analyzed using Mann-Whitney U test, Kruskal Wallis test, Pearson's correlation coefficient and hierarchical multiple regression analysis. Results: Mean age of the participant was $67.81{\pm}6.94$. Mean years since diagnosis was $6.41{\pm}5.20$. The mean score of quality of life was $0.82{\pm}0.09$, indicating that QOL was relatively low. Lower urinary tract symptoms (p=.029), anxiety, depression, Activities of daily living were significantly correlated to with quality of life (p<.001). Activities of daily living accounted for 54% (p<.001) of the variance in quality of life as a result of hierarchical multiple regression analysis. Conclusion: The results of the study showed that nursing intervention may improve the quality of life of patients with benign prostatic hyperplasia by increasing their Activities of daily living.

• Imaging Science in Dentistry
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• v.50 no.3
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• pp.183-192
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• 2020

Purpose: This study was performed to assess the accuracy of preoperative cone-beam computed tomography (CBCT), when justified for other reasons, in locating the apical foramen and establishing the working length. Materials and Methods: Six electronic databases were searched for studies on this subject. All studies, of any type, were included if they compared measurements of working length with preoperative CBCT to measurements using an electronic apex locator (EAL) or histological reference standard. Due to the high levels of heterogeneity, an inverse-variance random-effects model was chosen, and weighted mean differences were obtained with 95% confidence intervals and P values. Results: Nine studies were included. Compared to a histological reference standard, CBCT indicated that the apical foramen was on average 0.40 mm coronal of its histological position, with a mean absolute difference of 0.48 mm. Comparisons were also performed to an EAL reference standard, but the conclusions could not be considered robust due to high levels of heterogeneity in the results. Conclusion: A low level of evidence is produced suggesting that preoperative CBCT shows the apical foramen to be on average 0.40 mm coronal to its histological position, with a mean absolute difference of 0.48 mm.