• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.2
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• pp.358-365
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• 2001

The purpose of this study was to investigate the relation between various aspects of multiple body image and clothing behavior i.e. individuality/self expression, body improvement, social approval, sex appeal. The data were collected from 498 female university students in Seoul and Kyong Ki Province and analyzed using factor analysis, Pearsons correlations, reliability test, analysis of variance, and multiple regression analysis. The results of this study were as follows: 1) Four dimensions of multiple body image were identified : appearance, body attractiveness, degree of fitness and atheletic skill. 2) Perception on appearance and fitness aspect of multiple body image has a positive correlation with all aspects of clothing behavior i.e. individuality/self expression, body improvement, social approval and sex appeal of clothing behavior. Body attractiveness and atheletic skill of multiple body image also had a positive correlation with individuality/self expression, and sex appeal.

• The Journal of Korean Academy of Prosthodontics
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• v.46 no.6
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• pp.610-619
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• 2008

STATEMENT OF PROBLEM: Factors affecting patients' decision-making for dental prosthetic treatment should be examined in terms of understanding improving patients' oral health. PURPOSE: The main purpose of this dissertation was to investigate patients' dental prosthetic treatment and factors affecting patients' decision-making for dental prosthesis treatment in Deagu and Gyungbook areas. MATERIAL AND METHODS: This study was based on the preliminary survey of dental patients conducted from July 1 to August 31 in 2006. A total of 700 questionnaires had been distributed and 640 were collected. 629 questionnaires were used for the statistical analysis. Descriptive and inferential statistics, such as frequencies, cross tabulation analysis, correlation analysis, logistic regression analysis, and multiple regression analysis were introduced. In the multiple regression analysis and logistic regression analysis, twenty-two independent variables were employed to explore the factors which have impacts on decision-making and satisfaction. RESULTS: The results of this dissertation are as follows: Logistic regression analysis turned out that monthly income, age, degree of expectation, marital status, and employer-insured policy of national insurance statistically increased the odds of decision-making of dental prosthesis treatment. But educational attainment decreased the odds ratio of the decision-making of dental prosthesis treatment. However, the rest independent variables do not have statistically significant impacts on the decision-making of dental prosthesis treatment CONCLUSION: Among independent variables, marital status had the most significant influence on the decision making of dental prosthesis treatment. Finally, suggestions for the future study and policy implications to improve satisfaction of the patients' dental prosthetic treatment were discussed.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.10
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• pp.1542-1549
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• 2018

Objective: Because lactation periods in dairy cows lengthen with increasing total milk production, it is important to predict individual productivities after 305 days in milk (DIM) to determine the optimal lactation period. We therefore examined whether the random regression (RR) coefficient from 306 to 450 DIM (M2) can be predicted from those during the first 305 DIM (M1) by using a RR model. Methods: We analyzed test-day milk records from 85,690 Holstein cows in their first lactations and 131,727 cows in their later (second to fifth) lactations. Data in M1 and M2 were analyzed separately by using different single-trait RR animal models. We then performed a multiple regression analysis of the RR coefficients of M2 on those of M1 during the first and later lactations. Results: The first-order Legendre polynomials were practical covariates of RR for the milk yields of M2. All RR coefficients for the additive genetic (AG) effect and the intercept for the permanent environmental (PE) effect of M2 had moderate to strong correlations with the intercept for the AG effect of M1. The coefficients of determination for multiple regression of the combined intercepts for the AG and PE effects of M2 on the coefficients for the AG effect of M1 were moderate to high. The daily milk yields of M2 predicted by using the RR coefficients for the AG effect of M1 were highly correlated with those obtained by using the coefficients of M2. Conclusion: Milk production after 305 DIM can be predicted by using the RR coefficient estimates of the AG effect during the first 305 DIM.

• Advances in Computational Design
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• v.7 no.2
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• pp.113-128
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• 2022

Water consumption is strongly affected by numerous factors, such as population, climatic, geographic, and socio-economic factors. Therefore, the implementation of a reliable predictive model of water consumption pattern is challenging task. This study investigates the performance of predictive models based on multi-layer perceptron (MLP), multiple linear regression (MLR), and support vector regression (SVR). To understand the significant factors affecting water consumption, the stepwise regression (SW) procedure is used in MLR to obtain suitable variables. Then, this study also implements three predictive models based on these significant variables (e.g., SWMLR, SWMLP, and SWSVR). Annual data of water consumption in Thailand during 2006 - 2015 were compiled and categorized by provinces and distributors. By comparing the predictive performance of models with all variables, the results demonstrate that the MLP models outperformed the MLR and SVR models. As compared to the models with selected variables, the predictive capability of SWMLP was superior to SWMLR and SWSVR. Therefore, the SWMLP still provided satisfactory results with the minimum number of explanatory variables which in turn reduced the computation time and other resources required while performing the predictive task. It can be concluded that the MLP exhibited the best result and can be utilized as a reliable water demand predictive model for both of all variables and selected variables cases. These findings support important implications and serve as a feasible water consumption predictive model and can be used for water resources management to produce sufficient tap water to meet the demand in each province of Thailand.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.26 no.4
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• pp.181-192
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• 2023

Purpose: The development of assistive devices has allowed for the performance of capsule endoscopy in children. Anticipating the capsule's transit time could affect the efficacy of the investigation and potentially minimize the fasting period. This study determined the predictors of small bowel transit time for small-bowel capsule endoscopy in children and adolescents with inflammatory bowel disease. Methods: We retrospectively examined children and adolescents with inflammatory bowel disease who underwent capsule endoscopy by the age 18 at a Japanese tertiary care children's hospital. Small bowel transit time predictors were analyzed using multiple regression with explanatory variables. Results: Overall, 92 patients, aged 1-17 years, with inflammatory bowel disease (63 Crohn's disease and 29 ulcerative colitis cases) were examined for factors affecting small bowel transit time. In the simple regression analysis, diagnosis, age, height, weight, serum albumin, general anesthesia, and small intestine lesions were significantly associated with small bowel transit time. In the multiple regression analyses, serum albumin (partial regression coefficient: -58.9, p=0.008), general anesthesia (partial regression coefficient: 127, p<0.001), and small intestine lesions (partial regression coefficient: 30.1, p=0.037) showed significant associations with small bowel transit time. Conclusion: Hypoalbuminemia, the use of general anesthesia for endoscopic delivery of the capsule, and small intestine lesions appeared to be predictors of prolonged small bowel transit time in children and adolescents with inflammatory bowel disease. Expecting the finishing time may improve examination with a fasting period reduction, which benefits both patients and caregivers.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.2
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• pp.647-652
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• 2015

Background: Lung cancer was one of the most common cancers in both men and women all over the world. In this study, we aimed to clarify who could survive after long term chemotherapy in patients with advanced non-small cell lung cancer (NSCLC). Methods: We enrolled 186 patients with stage IV NSCLC after long term chemotherapy from Jun 2006 to Nov 2014 diagnosed in Jiangsu Cancer Hospital. Multiple variables like age, gender, smoking, histology of adenocarcinoma and squamous-cell cancer, number of metastatic sites, metastatic sites (e.g. lung, brain, bone, liver and pleura), hemoglobin, lymphocyte rate (LYR), Change of LYR during multiple therapies, hypertension, diabetes, chronic bronchitis, treatments (e.g.radiotherapy and targeted therapy) were selected. For consideration of factors influencing survival and response for patients with advanced NSCLC, logistic regression analysis and Cox regression analysis were used in an attempt to develop a screening module for patients with elevated survival after long term chemotherapy become possible. Results: Of the total of 186 patients enrolled, 69 survived less than 1 year (short-term group), 45 one to two years, and 72 longer than 3 years (long-term group). For logistic regression analysis, the short-term group was taken as control group and the long-term group as the case group. We found that age, histology of adenocarcinoma, metastatic site (e.g. lung and liver), treatments (e.g. targeted therapy and radiotherapy), LYR, a decreasing tendency of LYR and chronic bronchitis were individually associated with overall survival by Cox regression analysis. A multivariable Cox regression model showed that metastatic site (e.g. lung and liver), histology of adenocarcinoma, treatments (e.g. targeted therapy and radiotherapy) and chronic bronchitis were associated with overall survival. Thus metastatic site (e.g. lung and liver) and chronic bronchitis may be important risk factors for patients with advanced NSCLC. Gender, metastatic site (e.g. lung and liver), LYR and the decreasing tendency of LYR were significantly associated with long-term survival in the individual-variable logistic regression model (P<0.05). On multivariate logistic regression analysis, gender, metastatic site (e.g. lung and liver) and the decreasing tendency of LYR associated with long-term survival. Conclusions: In conclusion, female patients with stage IV adenocarcinoma of NSCLC who had decreasing tendency of LYR during the course therapy and had accepted multiple therapies e.g. more than third-line chemotherapy, radiotherapy and/or targeted therapy might be expected to live longer.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.3
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• pp.147-159
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• 2014

Public transportation ridership and walkability of urban district can be enhanced through high quality of TOD(Transit Oriented Development) elements. Generally, TOD have been evaluated several physical components such as the diversity of land use pattern, accessibility of public transportation and aspects of urban design around the station area. Especially, Spatial characteristics of TOD planning elements have many potential dependent when considering the characteristics of Rail Station-Influenced Area Development which is performing around subway station. Therefore, researchers should be considering the variation of spatial properties for planning elements according the set of spatial area and their socioeconomic factors. However, existing many cases related TOD does not consider about this point. In this paper, the changes of TOD characteristics were analyzed by different spatial units surrounding subway station in Busan Metropolitan City. Multiple Regression Analysis was performed for an investigation of effective spatial unit of TOD planning elements in this area using subway ridership data. In addition, the application validity of socioeconomic variables was examined through a comparative analysis of regression results with the multiple regression that implied only physical TOD elements. As the result, the variation of spatial properties for TOD planning elements according to the set of spatial unit was found. Furthermore, the specific spatial unit to applicable TOD elements in this area was derived. And the multiple regression model which added socioeconomic variables was derived more improved estimate results than the multiple regression model that implied only physical TOD elements.

• Journal of Distribution Science
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• v.13 no.8
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• pp.91-96
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• 2015

Purpose - This study reviews the delivery of child welfare service quality and examines how the dimensions of the variables of customer satisfaction impact the results from a service distribution perspective. This study differs from existing research since it proposes that a recognized level of child welfare service quality is necessary to achieve customer satisfaction from the perspective of service distribution. Research design, data, and methodology - This study explores child welfare service quality factors that affect customer satisfaction. The study examines and analyzes demographic variables, service quality dimensions, and the causal relationships between child welfare service quality and customer satisfaction. Data from 300 child welfare cases were collected from organizations in Korea in the areas of Busan and Gyeongsangnamdo. The methods of analysis are as follow. First, using descriptive analysis frequency, the percentages were evaluated to assess the demographic variables. Second, Cronbach's α was used to test reliability and to evaluate the internal consistency of the measuring of items. Third, multiple regression analysis was conducted to find out how much the independent variable can affect customer satisfaction. Results - Five factors of child welfare service quality were identified in three categories: process quality (assurance, empathy), results quality (reliability, caring), and physical environment quality (tangibles). There were significant differences among the effects of the child welfare service quality factors on customer satisfaction. A multiple regression analysis was done with process quality (assurance, empathy), results quality (reliability, caring) and physical environment quality (tangibles) to test the hypothesis: assurance (t=2.434, p<0.05), empathy (t=3.677, p<0.001), reliability (t=3.271, p<0.05), caring (t=4.380, p<0.000), and tangibles (t=3.654, p<0.01) had a positive influence on child welfare service quality from a service distribution perspective. Therefore, hypotheses 1, 2, 3, 4, and 5 were supported. In addition, multiple regression analysis on the effects of the variables showed that caring (β=0.273), empathy (β=0.246), tangibles (β=0.265), reliability (β=0.152), and assurance (β=0.131) all had a positive and strong influence on child welfare service quality from a service distribution perspective. Therefore, all child welfare service quality categories (process, results and physical environment quality) were positively statistically significant. Conclusion - In this study, the main findings can be summarized as follows. First, the quality of service of child welfare consists of three dimensions of quality: process quality, results quality, and physical environment quality. The results of the multiple regression analysis also showed that caring and reliability were confirmed as more meaningful variables by the increasing loading factors. Second, the family members involved in child welfare proposed caring as the most important variable among the dimensions of service quality. Third, the results of the hypothesis testing using regression showed that all child welfare service quality factors had a positive impact on customer satisfaction. The results of the study could provide useful information to help increase the effectiveness of delivery strategies for child welfare service quality from a service distribution perspective.

• Journal of Environmental Health Sciences
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• v.48 no.4
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• pp.236-243
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• 2022

Background: In the case of multiple exposures to different types of heavy metals, such as the conditions faced by residents living near a smelter, it would be preferable to group hazardous substances with similar characteristics rather than individually related substances and evaluate the effects of each group on the human body. Objectives: The purpose of this study is to evaluate the utility of factor analysis in the assessment of health effects caused by exposure to two or more hazardous substances with similar characteristics, such as in the case of residents living near a smelter. Methods: Heavy metal concentration data for 572 people living in the vicinity of the Janghang smelter area were grouped based on several subfactors according to their characteristics using factor analysis. Using these factor scores as an independent variable, multiple regression analysis was performed on health effect markers. Results: Through factor analysis, three subfactors were extracted. Factor 1 contained copper and zinc in serum and revealed a common characteristic of the enzyme co-factor in the human body. Factor 2 involved urinary cadmium and arsenic, which are harmful metals related to kidney damage. Factor 3 encompassed blood mercury and lead, which are classified as related to cardiovascular disease. As a result of multiple linear regression analysis, it was found that using the factor index derived through factor analysis as an independent variable is more advantageous in assessing the relevance to health effects than when analyzing the two heavy metals by including them in a single regression model. Conclusions: The results of this study suggest that regression analysis linked with factor analysis is a good alternative in that it can simultaneously identify the effects of heavy metals with similar properties while overcoming multicollinearity that may occur in environmental epidemiologic studies on exposure to various types of heavy metals.