• Korean Journal of Community Nutrition
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• v.3 no.5
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• pp.679-684
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• 1998

This study was conducted to investigate the determinants of obesity during adolescnece. A total of 726 adolescents living in rural areas in Korea had been observed for four years from 1992 to 1996 regarding their diet, sexual maturity, blood profile and physical growth. Stepwise multiple regression analysis was used to identify priorities fo the importance between the factors influencing obesity. The average nutrient intake over the three year period was higher than that of the Korean Recommended Dietary Allowances. The prevalence of obesity for the subjects based on BMI was 9.5%. Results of the stepwise multiple regression analysis showed that blood components and sexual maturity were more significant factors for determining the obesity than the dietary factors. The result may suggest that to understand obesity in children it is necessary to develop on analytical model for the children rather than using the existing analytical model developed mostly for adult patients of obesity. The model should include a wide range of variables such as diet, sexual maturity and changes in blood.

• The Journal of Korean Society for School & Community Health Education
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• v.14 no.3
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• pp.51-65
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• 2013

Objectives: The main purpose of this study was to compare influencing factors on depression between male and female students at the high school. Methods: The self-administered questionnaire survey was carried out on a convenience sample of 403 high school students. The data analysis procedure included frequency, ${\chi}^2$ test, t-test, ANOVA, Pearson correlation coefficients, and stepwise multiple regression using depression as the dependent variable. Results: There was a significant gender difference in depressive symptoms; the mean depression score of female students was higher than that of male students. Stepwise multiple regression analysis for depression revealed that the most powerful predictors (34%) were powerlessness and self-esteem for male students. On the other hand, the factors such as self-esteem, mother's occupation, and family fucntion were the most significant predictors (50%) for female students. Conclusion: The necessity of an intervention considering gender difference in high school students so as to prevent the occurrence of depression was suggested.

• Journal of Korean Academy of Nursing
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• v.31 no.4
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• pp.561-570
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• 2001

Purpose: This study was conducted to detect the correlations and the main factors influencing depression, life satisfaction, burden, defenition of suffering, meaning of life, and suffering. Method: The samples were composed of 160 cancer patients who were or outpatients of four hospitals in Seoul. The reliability of the 6 instruments was tested with Cronbach's alpha which ranged from .62 to .90. The data was analyzed using a SAS program for descriptive statistics, Pearson correlation coefficients, and stepwise multiple regression. Results: The results were as follows: 1. The scores on the suffering scale ranged from 132 to 40 with a mean of 87.3 (SD 17.5). 2. There were significant correlations between all the predictive variables and even the amounts of suffering (r=.27-.84, p〈.05). 3. Stepwise multiple regression analysis showed that depression was the main predictor of suffering, and accounted for 71.6% of the variance. In addition burden accounted for 4.6% of the variance in suffering. The two variables combined to account for 76.2% of the variance in suffering. Conclusion: In conclusion and depression, burden were identified as important variables in explaining the suffering of patients with cancer.

• Korean Journal of Social Welfare
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• v.45
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• pp.288-314
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• 2001

The purpose of this study is to examine the strategies that may influence the marital satisfaction of dual-earner men and women. General linear model, Pearson's correlation analysis, Stepwise multiple regression were employed for data analysis. the subjects are 396 dual-earner men and women. The result from the research were as follows: 1) coping strategy use differs significantly by life cycle stage. 2) The following strategies significantly correlated with the level of marital satisfaction: cognitive restructuring, delegation. using social support, modifying standards, personal time reducing. 3) The result of stepwise multiple regression analysis indicated that strategies which predict the level of marital satisfaction were cognitive restructuring, delegating, using social support, personal time reducing. these finding give us significant practical implications for social work intervention.

• The Journal of Korean Academic Society of Nursing Education
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• v.20 no.4
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• pp.471-481
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• 2014

Purpose: This study investigates the relationship among stress, Yangsaeng and subjective happiness in female undergraduate nursing students in the Republic of Korea. Method: The subjects of this study were 283 female undergraduate nursing students in Korea. Data were collected using a self-reporting questionnaire. Data analysis included descriptive statistics, Pearson's correlation coefficients, independent t-tests, one-way ANOVA, Scheffe test, Stepwise multiple regression and Cronbach's ${\alpha}$. Results: Yangsaeng negatively correlated with stress (r=-.299, p<.001) but positively correlated with subjective happiness (r=.440, p<.001). Stress negatively correlated with subjective happiness (r=-.238, p<.001). Stepwise multiple regression revealed that Yangsaeng and satisfaction with the field of nursing explained 25.4% of subjective happiness. Conclusion: The Yangsaeng oriental health care regimen could lower stress and heighten subjective happiness. Therefore, Yangsaeng could be recommended as a feasible means of promoting health and subjective happiness.

• Journal of Korean Academy of Nursing Administration
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• v.12 no.2
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• pp.305-310
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• 2006

Purpose: This study was to identify affecting factors on job satisfaction, nursing professional attitudes of new nurses according to transformational leadership of preceptors. Method: The subjects were 101 new nurses who were working for the 4 of university hospitals in Seoul, Daejeon and Kyong Ki. This study was conducted from Aug to Oct 2005. The data was collected by questionnaires and were analyzed using descriptive statistics, pearson correlation coefficient, stepwise multiple regression. Result: Transformational leadership of preceptors were significantly correlated between Job satisfaction, nursing professional attitudes of the new nurses. The stepwise multiple regression analysis for job satisfaction, nursing professional attitudes revealed that the most powerful predictor was charisma. Conclusion: The findings indicated that the transformational leadership of preceptors is important for improving job satisfaction, nursing professional attitudes of the new nurses. Therefore it may be necessary further to develop of the leadership training program for effective preceptorship.

• IE interfaces
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• v.23 no.1
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• pp.78-88
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• 2010

In this paper we develop a model for the performance evaluation of defense R&D test development projects based on analytic hierarchy process. First, evaluation indicators are collected through the related literature survey and a delphi inquiry method. Second, stepwise multiple linear regression is used for developing a hierarchical structure for analytic hierarchy process in the evaluation model, which can make the selected evaluation indicators of the hierarchical structure independent. Also we verify the effectiveness of proposed indicators of the performance evaluation by comparing with the existing evaluation indicators. The developed indicators for the performance evaluation is more reasonable and practical than the previous indicators on defense R&D test development projects.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4189-4200
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• 2021

In this study, the relationship between the environmental radiation dose rate and meteorological variables was investigated with multiple regression analysis and big data of those variables. The environmental radiation dose rate and 36 different meteorological variables were measured on Ulleung Island, Republic of Korea, from 2011 to 2015. Not all meteorological variables were used in the regression analysis because the different meteorological variables significantly affect the environmental radiation dose rate during different periods, and the degree of influence changes with time. By applying the Pearson correlation analysis and stepwise selection methods to the big dataset, the major meteorological variables influencing the environmental radiation dose rate were identified, which were then used as the independent variables for the regression model. Subsequently, multiple regression models for the monthly datasets and dataset of the entire period were developed.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.4
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• pp.269-273
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• 2006

Multiple linear regression analysis was conducted to interpretate the relationship between sesame grain yield and its components under early sowing cropping condition. The t test showed that stem length, number of capsules per plant, 1000 seeds weight and seed weight per plant gave significant contribution to sesame grain yield, therefore those variables were assumed to mostly influenced components to grain yield of sesame. In the stepwise regression analysis, the predicted equation for sesame grain yield per square meter (Y) was Y = -7.900 + 0.150X1 + 0.461X5 + 15.553X6 + 8.543X7. Meanwhile, F value showed that stem length, number of capsules per plant and seed weight per plant gave significant contribution to sesame grain yield, while 1000 seeds weight did not significantly show. Based on the results, it is reasonable to assume that high yield. potential of sesame under early sowing cropping condition would be obtained by selecting breeding lines with long stem length, number of capsules per plant, and seed weight per plant, which was different result at the late sowing cropping condition in which days to flowering and maturity were assumed to be more affected factors to the sesame grain yield.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.39-50
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• 2006

The flood control offices of main rivers have used a storage function model to forecast flood stage in Korea and studies of flood forecasting actively have been done even now. On this account, the storage function model, which is used in flood control office, regression models and artificial neural network model are applied into flood forecasting of study watershed in this paper. The result obtained by each method are analyzed for the comparative study. In case of storage function model, this paper uses the representative parameters of the flood control offices and the optimized parameters. Regression coefficients are obtained by regression analysis and neural network is trained by backpropagation algorithm after selecting four events between 1995 to 2001. As a result of this study, it is shown that the optimized parameters are superior to the representative parameters for flood forecasting. The results obtained by multiple, robust, stepwise regression analysis, one of the regression methods, show very good forecasts. Although the artificial neural network model shows less exact results than the regression model, it can be efficient way to produce a good forecasts.