• The Korean Journal of Health Service Management
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• v.13 no.4
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• pp.133-144
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• 2019

Objectives: To analyze the radiologic results of patients treated with bone defects treated with beta-tricalcium phostphate (β-TCP) in orthopedic surgery. Methods: Medical records of 49 patients, consisting of 24 (49.0%) men and 25 (51.0%) women were retrospectively reviewed. Graft incorporation was evaluated based on Irwin's radiologic staging. Results: The explanatory power of the total regression equation in Irwin's stage at 3 months postoperatively according to three explanatory variables, namely graft tightness, age at diagnosis, and lesion volume, was 65.6%; the explanatory power of Irwin's stage at 6 months postoperatively according to two variables, namely lesion volume and graft tightness, was 32.9%; and the explanatory power of Irwin's stage at 9 months postoperatively for two variables, namely longitudinal lesion length and graft tightness in the total regression, was 30.8%. Conclusions: Graft tightness, lesion volume, age at diagnosis, and longitudinal lesion length are the common factors affecting graft incorporation.

• The International Journal of Costume Culture
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• v.6 no.2
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• pp.93-104
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• 2003

The purpose of this study was to investigate the relationship between women's face satisfaction and makeup satisfaction, to disclose the differences of makeup satisfaction according to demographic variables, and to examine how makeup satisfaction was influenced by face satisfaction and demographic variables. The subjects were 200 women over age 17 living in Seoul and its peripheral areas. The results of this study were as follows: Face satisfaction were drawn three factors. Factor 1 was face contour satisfaction, Factor 2 was skin satisfaction, and Factor 3 was lips and eyes satisfaction. There were significant positive relationship between factors of face satisfaction and makeup satisfaction. Also, the face contour satisfaction was in positive correlation with satisfaction of features, and the skin satisfaction was in positive correlation with that of features. There were significant positive correlations between makeup satisfaction and face shape, eyes, nose, lips, chin, and cheek bone satisfaction. Face satisfaction didn't show significant difference according to demographic variables, but makeup satisfaction showed significant difference according to age and occupation. Face satisfaction was influenced by the facial face, clarity of skin, elasticity of skin, skin color, and ages. The explanatory power of the 4 variables were 24.5%. Makeup satisfaction was influenced by lips and eyes satisfaction, ages, and skin care level. The explanatory power of the 3 variables were 13.3%.

• Korean Journal of Occupational Health Nursing
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• v.28 no.2
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• pp.94-103
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• 2019

Purpose: This study aimed to investigate variables affecting health-related quality of life of manufacturing industry workers. Methods: We used data from 200 survey questionnaires collected from a vehicle manufacture factory located in Ulsan metropolitan area. The participants were manufacturing workers. The data were analyzed using t-test, one-way analysis of variance, and $Scheff{\acute{e}}^{\prime}s$ test to identify differences among variables. Pearson's correlation coefficient and stepwise multiple regression were used to identify variables that affected health-related quality of life. Results: The findings revealed that occupational stress and quality of sleep were statistically significant variables of the physical component score with an explanatory power of 42.2%. Occupational stress, depression, and religion were statistically significant variables of the mental component score with an explanatory power of 43.3%. Conclusion: Continuous monitoring and appropriate intervention for occupational stress, quality of sleep, and depression are needed to improve manufacturing workers' health-related quality of life.

• Asia-Pacific Journal of Business
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• v.14 no.1
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• pp.411-426
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• 2023

Purpose - The purpose of this study was to explore and diagnose the characteristics and behavioural patterns of rural tourists after COVID-19 using decision tree analysis to classify and identify key segmentation groups. Design/methodology/approach - The CHAID algorithm was used as the analysis technique for the decision tree. The explanatory variables used in the analysis of each decision tree model were demographic variables and rural tourism usage behaviour and perception variables, and the target variables were the preferences of rural tourists' activities after COVID-19. From the Rural Tourism 2020 survey data, 614 samples with rural tourism experience were extracted and used in the analysis. Findings - The variables that significantly explained the preference for each type of rural tourism activity after COVID-19 were rural tourism safety perception, repeated visits to the region, rural tourism priority activity, rural tourism accommodation experience, gender, age group, marital status, occupation, and education level. Among them, rural tourism safety perception was the most important explanatory variable in each analysis model. Research implications or Originality - Overall, to promote rural tourism, it is necessary to enhance the safety image of rural tourism, strengthen loyalty programs for repeat visitors, and develop customized products that reflect the preferred trends of rural tourism.

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.935-943
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• 2004

Most of the accelerated life tests deal with tests that use only one accelerating variable and no other explanatory variables. Frequently, however, there is a test to use more than one accelerating or other experimental variables, such as, for examples, a test of capacitors at higher than usual conditions of temperature and voltage, a test of circuit boards at higher than usual conditions of temperature, humidity and voltage. A accelerated life test is extended to M-level stress accelerated life test with k-stress variables. The optimal design for Weibull distribution is studied with k-stress variables.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1409-1416
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• 2015

The change of temperature influences on the various aspect, especially human health, plant and animal's growth, economics, industry, and culture of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly temperature data at the Suwon monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). Among five meteorological variables, radiation, amount of cloud, and wind speed are more influence on the temperature. The radiation influences during spring, summer and fall, whereas wind speed influences for the winter time. Also, among four greenhouse gas variables and five pollution variables, chlorofluorocarbon, methane, and ozone are more influence on the temperature. The monthly ARE model explained about 43-69% for describing the temperature.

• Korean Journal of Veterinary Service
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• v.32 no.3
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• pp.265-273
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• 2009

A cross-sectional survey was undertaken to identify risk factors and clinical signs associated with parasitic helminth infections of cattle in Mymensignh district of Bangladesh. A nonrandom convenience sampling method was used to select 138 animals from 40 farmers/herds. The eggs per gram of faeces (epg) for nematodes and trematodes were determined by McMaster and Stoll's methods respectively. Animal-level and herd-level data were recorded by means of a questionnaire. Multi-collinearity amongst explanatory variables were assessed using $2{\times}2{\times}\;X^2$ test and one variable in a pair was dropped if $P{\leq}0.05$ formultiple logistic regression models. Association study between outcome and explanatory variables was conducted using classification tree, random forests and multiple logistic regression. A positive epg was considered as infected. Analyses were performed using $STATA^{(R)}$, version 8.0/Intercooled and $R^{(R)}$, Version 2.3.0. Seventy eight percent of the cattle were found to be infected with at least one type of helminth. Twenty four pairs of combinations of explanatory variables showed significant associations. Male animals (OR=3.3, P=.006, 95% CI=1.4, 7.7) were associated with significantly increased prevalence of nematode infection. Female cattle of the study area are mostly cross-breed, kept indoor, fed relatively good diet and not used for draught purpose. Males are used for draught purpose thereby more exposed to nematode infective stage and provided with relatively poor diet. So stressed male cattle may become more susceptible to nematode infection. All of the three statistical techniques selected gender and lumen motility as most important variables in association with nematode infection in cattle. The result of this survey can only be extrapolated to the periurban cattle population of traditional management system.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.279-289
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• 2010

In this study, we propose a simple statistical model for determining the admission or discharge of 668 patients with a chief complaint of dyspnea. For this, we use 11 explanatory variables which are chosen to be important by clinical experts among 55 variables. As a modification process, we determine the discharge interval of each variable by the kernel density functions of the admitted and discharged patients. We then choose the optimal model for determining the discharge of patients based on the number of explanatory variables belonging to the corresponding discharge intervals. Since the numbers of the admitted and discharged patients are not balanced, we use, as the criteria for selecting the optimal model, the arithmetic mean of sensitivity and specificity and the harmonic mean of sensitivity and precision. The selected optimal model predicts the discharge if 7 or more explanatory variables belong to the corresponding discharge intervals.

• International Journal of Highway Engineering
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• v.14 no.5
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• pp.189-199
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• 2012

PURPOSES: The purpose of this study is to propose a new methodology for developing statistical collision models and to show the validation results of the methodology. METHODS: A new modeling method of introducing variables into the model one by one in a multiplicative form is suggested. A method for choosing explanatory variables to be introduced into the model is explained. A method for determining functional forms for each explanatory variable is introduced as well as a parameter estimating procedure. A model selection method is also dealt with. Finally, the validation results is provided to demonstrate the efficacy of the final models developed using the method suggested in this study. RESULTS: According to the results of the validation for the total and injury collisions, the predictive powers of the models developed using the method suggested in this study were better than those of generalized linear models for the same data. CONCLUSIONS: Using the methodology suggested in this study, we could develop better statistical collision models having better predictive powers. This was because the methodology enabled us to find the relationships between dependant variable and each explanatory variable individually and to find the functional forms for the relationships which can be more likely non-linear.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.241-247
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• 2021

This study analyzed the changes in explanatory power of the modified Jones model(1995) for estimating the amount of accruals for Korean Stock Market listed companies from 1990 to 2019. We hypothesized that if the properties of financial variables used in the existing model change over time or change in discretionary ratios, the model's explanatory power will change. As the result of regression models, I found that the explanatory power of the modified Jones model(1995) gradually declined over time. The results may be derived from the increase in accruals itself and the changes in the distribution of variables contained in the model. The results of this research's chronological approach are expected to give important implications to both academic researchers and accounting information users.