• Journal of Preventive Medicine and Public Health
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• v.46 no.1
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• pp.28-38
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• 2013

Objectives: This study aimed to collect information that will help enhance the social networks and improve the quality of life among elderly people by observing the relationship between their social network and health-related quality of life (HRQoL) and by analyzing social network factors affecting HRQoL. Methods: This study was based on the 2008 Community Health Survey in Yeoncheon County. Three hundred elders were included in the study population. We compared the revised Lubben Social Network Scale (LSNS-R) score and Euro quality of life-5 dimensions health status index by demographic characteristics and chronic disease prevalence. We analyzed the data using multiple regression and tobit regression by setting the HRQoL as the dependent variable and social network and other characteristics as the independent variables. We analyzed social network factors by using factor analysis. Results: The LSNS-R score differed significantly according to age and existence of a spouse. According to the results from the hierarchical multiple regression analysis, the LSNS-R explained 0.10 of the variance and LSNS-R friends factor explained 0.10 of the variance. The tobit regression indicated that the contribution of the LSNS-R family size factor to the regression coefficient of the independent variable that affected the HRQoL was $B_T$=2.96, that of the LSNS-R family frequency factor was $B_T$=3.60, and that of LSNS-R friends factor was $B_T$=5.41. Conclusions: Social networks among elderly people had a significant effect on HRQoL and their networks of friends had a relatively higher effect than those of family members.

• Structural Engineering and Mechanics
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• v.81 no.5
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• pp.565-574
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• 2022

The infrastructures such as offshore, bridges, power plant, oil and gas piping and aircraft operate in a harsh environment during their service life. Structural integrity of engineering components used in these industries is paramount for the reliability and economics of operation. Two regression models based on the concept of Gaussian process regression (GPR) and Minimax probability machine regression (MPMR) were developed to predict stress intensity factor range (𝚫K). Both GPR and MPMR are in the frame work of probability distribution. Models were developed by using the fatigue crack growth data in MATLAB by appropriately modifying the tools. Fatigue crack growth experiments were carried out on Eccentrically-loaded Single Edge notch Tension (ESE(T)) specimens made of API 5L X65 Grade steel in inert and corrosive environments (2.0% and 3.5% NaCl). The experiments were carried out under constant amplitude cyclic loading with a stress ratio of 0.1 and 5.0 Hz frequency (inert environment), 0.5 Hz frequency (corrosive environment). Crack growth rate (da/dN) and stress intensity factor range (𝚫K) values were evaluated at incremental values of loading cycle and crack length. About 70 to 75% of the data has been used for training and the remaining for validation of the models. It is observed that the predicted SIF range is in good agreement with the corresponding experimental observations. Further, the performance of the models was assessed with several statistical parameters, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Efficiency (E), Root Mean Square Error to Observation's Standard Deviation Ratio (RSR), Normalized Mean Bias Error (NMBE), Performance Index (ρ) and Variance Account Factor (VAF).

• Journal of Environmental Impact Assessment
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• v.23 no.3
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• pp.187-196
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• 2014

The objective of this study is to evaluate and analyze Osu stream watershed water environment system. The data were collected from January 2009 to December 2011 including water temperature, pH, DO, EC, BOD, COD, TOC, SS, T-N, T-P and discharge. The data were used for principle component analysis and factor analysis. The results are as followes. The primary factors obtained from both the principal component analysis and the factor analysis were BOD, COD, TOC, SS and T-P. Once principal component analysis and factor analysis have been performed with the collected data and then the results will be applied to both simple regression model and multiple regression model. The regression model was developed into case 1 using concentrations of water quality parameters and case 2 using delivery loads. The value of the coefficient of determination on case 1 fell between 0.629 and 0.866; this was lower than case 2 value which fell between 0.946 and 0.998. Therefore, case 2 model would be a reliable choice.The coefficient of determination between the estimated figure using data which was developed to the regression model in 2012 and the actual measurement value was over 0.6, overall. It can be safely deduced that the correlation value between the two findings was high. The same model can be applied to get TOC concentrations in future.

• Fashion & Textile Research Journal
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• v.4 no.1
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• pp.11-18
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• 2002

The purpose of this study was to investigate the characteristic and composition factor of Japanese costume design. The stimulus were 25 contemporary costume design which represented the traditional image of Japanese. The main survey of questionary consisted of their evaluation of the Japanese costume image by 26 semantic differential bipolar scales and the subjects were 99 female students majoring in clothing and textiles. The data were analyzed by Factor analysis, Multidimensional Scaling Method and Regression Analysis. The major findings were as follows. As a result of design analysis, contemporary Japanese costume design which represented the traditional image had traditional form, color, texture, pattern, etc. Through factor analysis about Japanese costume image 7 factors were identified; Attractiveness, Attention, Cool and warm, Neatness, Activeness, Maturity, Classics. According to image positioning, Japanese costume design was classified by simple-decorative, soft-hard. As the result of regression analysis, The preference of Japanese costume image was related to attractive factor.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.2
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• pp.375-381
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• 2015

In the past few years, data leakage of information assets has become prominent issue. According to the NIS in South Korea, they found 375 cases of data leakage from 2003 to 2013, especially 49 of cases have been uncovered in 2013 alone. These criminals are increasing as time passes. Thus, it constitutes a reason for establishment, operation and certification of ISMS, even for private enterprises. The purpose of this study is to examine the factors influencing the certification intention of ISMS using EFA (Exploratory Factor Analysis) and regression analysis. We identified expectation factors for certification of ISMS from 13 elements using EFA (Strengthening practical ability & economic effect factor and Improvement of security level & handling incident factor). Next, we examined that the certification intention of ISMS using regression analysis. As a result of regression analysis, Strengthening practical ability & economic effect factor is not significant for the certification intention of ISMS (p<.05). Also, Improvement of security level & handling incident factor have a significant and positive effect on the certification intention of ISMS (p<.05).

• Culinary science and hospitality research
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• v.14 no.3
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• pp.156-164
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• 2008

This study analyzed the effect of the environment of cooking education institutes on students' study satisfaction and re-registration in Busan, in order to provide those students with good education environment and useful information. The survey was conducted from March 25th to April 11th, 2008. 300 copies of the questionnaire were distributed and 293 copies were returned, among which 270 copies(unsuitable 23 copies were excluded from the analysis) were included as reliable statistical data for analysis. To figure out the result, frequency analysis, reliability verification(Cronbach's Alph), factor analysis and regression analysis were employed in this study. Analyzed factors included cooking environment factor, education service factor, additional factor and lecturer's attitude factor. For the regression analysis to find out the effect of cooking education environment factor on study satisfaction and re-registration, it was found that cooking environment factor, education service factor, additional factor and lecturer's attitude factor had significant effect on study satisfaction and re-registration, which meant that the assumptions 1, 2, 3, 4 and 5 were adopted in this analysis. For the regression analysis to find out the effect of the satisfaction for the environment of cooking education institutes on re-registration, it was found that study satisfaction had significant effects on re-registration and the assumption 6 was adopted. Through this study, it was suggested that the satisfaction and re-registration of cooking education institutes were influenced by all factors, especially for cooking environment and education service. Thus, it is necessary to improve the old environment for cooking education and cooking education programs. Also, continuous study should be conducted to secure potential customers in the future.

• Tunnel and Underground Space
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• v.14 no.2
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• pp.133-141
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• 2004

Through the many site investigations, different methods for evaluating stability of rock slopes have been proposed. Those methods, however, may lead to different results depending on the subjective judgments associated with the selection of the evaluation items and the application of weighting factor. Accordingly, binary logistic regression analysis was carried out to ensure fair appliction of the weighting factor, leading to an equation for evaluating the stability of rock slopes.

• Journal of the Korean Statistical Society
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• v.33 no.2
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• pp.169-189
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• 2004

Under the assumption of default priors, such as noninformative priors, Bayesian model determination and parameter estimation of regression models with stationary and invertible ARMA errors are developed under exact full likelihoods. The default Bayes factors, the fractional Bayes factor (FBF) of O'Hagan (1995) and the arithmetic intrinsic Bayes factors (AIBF) of Berger and Pericchi (1996a), are used as tools for the selection of the Bayesian model. Bayesian estimates are obtained by running the Metropolis-Hastings subchain in the Gibbs sampler. Finally, the results of numerical studies, designed to check the performance of the theoretical results discussed here, are presented.

• Journal of the Korean Statistical Society
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• v.26 no.2
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• pp.261-276
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• 1997

In the context of binary response regression, the problem of constructing Bayesian goodness-of-link test for testing logit link versus probit link is considered. Based upon the well known facts that cdf of logistic variate .approx. cdf of $t_{8}$/.634 and, as .nu. .to. .infty., cdf of $t_{\nu}$ approximates to that of N(0,1), Bayes factor is derived as a test criterion. A synthesis of the Gibbs sampling and a marginal likelihood estimation scheme is also proposed to compute the Bayes factor. Performance of the test is investigated via Monte Carlo study. The new test is also illustrated with an empirical data example.e.

• Transactions of Materials Processing
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• v.26 no.6
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• pp.341-347
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• 2017

Flexibly-reconfigurable roll forming (FRRF) is a novel sheet metal forming technology conducive to produce multi-curvature surfaces by controlling strain distribution along longitudinal direction. Reconfigurable rollers could be arranged to implement a kind of punch die set. By utilizing these reconfigurable rollers, desired curved surface can be formed. In FRRF process, three-dimensional surface is formed from two-dimensional curve. Thus, it is difficult to predict the forming result. In this study, a regression analysis was suggested to construct a predictive model for a longitudinal curvature of FRRF process. To facilitate investigation, input parameters affecting the longitudinal curvature of FRRF were determined as maximum compression value, curvature radius in the transverse direction, and initial blank width. Three-factor three-level full factorial experimental design was utilized and 27 experiments using FRRF apparatus were performed to obtain sample data of the regression model. Regression analysis was carried out using experimental results as sample data. The model used for regression analysis was a quadratic nonlinear regression model. Determination factor and root mean square root error were calculated to confirm the conformity of this model. Through goodness of fit test, this regression predictive model was verified.