• Journal of Korean Public Health Nursing
• /
• v.27 no.1
• /
• pp.76-88
• /
• 2013

Purpose: The purpose of this study was to identify the degree of sleep quality and its associated factors in adults. Methods: The data was collected from 986 adults aged 19 to 64 by convenience sampling. Subjects completed a questionnaire composed of Pittsburgh Sleep Quality Index (PSQI), Beck Depression Inventory, and other questions that self-rated health and sociodemographic variables. Statistical methods used included descriptive statistics, simple logistic regression, and multiple logistic regression analyses. Results: The global PSQI score was 5.7. About 45% of the subjects were poor sleepers (global PSQI score >5). Multiple logistic regression analyses showed that factors significantly associated with sleep quality were depression and poor self-rated health in young and middle-aged adults. Depression was the most significant associated factor. The presence of a spouse was also associated with sleep quality in young adults. Conclusion: These findings suggest that people with poor sleep quality should have their health carefully screened for depression. In addition, we recommend the development of a nursing program for improving sleep quality.

• Geomechanics and Engineering
• /
• v.14 no.4
• /
• pp.315-324
• /
• 2018

With rapid economic growth, numerous deep excavation projects for high-rise buildings and subway transportation networks have been constructed in the past two decades. Deep excavations particularly in thick deposits of soft clay may cause excessive ground movements and thus result in potential damage to adjacent buildings and supporting utilities. Extensive plane strain finite element analyses considering small strain effect have been carried out to examine the wall deflections for excavations in soft clay deposits supported by diaphragm walls and bracings. The excavation geometrical parameters, soil strength and stiffness properties, soil unit weight, the strut stiffness and wall stiffness were varied to study the wall deflection behaviour. Based on these results, a multivariate adaptive regression splines model was developed for estimating the maximum wall deflection. Parametric analyses were also performed to investigate the influence of the various design variables on wall deflections.

• Journal of the military operations research society of Korea
• /
• v.8 no.1
• /
• pp.71-76
• /
• 1982

A procedure is established for combining a regression model and a time series model to fit to a set of autocorrelated data. This procedure is based on an iterative method to compute regression parameter estimates and time series parameter estimates simultaneously. The time series model which is discussed is basically AR(p) model, since MA(q) model or ARMA(p,q) model can be inverted to AR({$\infty$) model which can be approximated by AR(p) model. The procedure discussed in this articled is applied in general to any combination of regression model and time series model.

• Korean Institute of Interior Design Journal
• /
• v.17 no.5
• /
• pp.99-106
• /
• 2008

Based on extraction items derived from previous related studies on viewing experiences in art museums, this study is conducted to investigate extraction factors that affect viewing satisfactions and to suggest a guideline for an effective viewing environment by clarifying a hierarchy among the extraction factors. For this study, a survey was given to museum visitors, and statistical analyses were conducted on data obtained from the survey. The results of this study are summarized as follows; 1. From an analysis of extraction items that affect overall viewing satisfaction, space and art works were found to be relatively satisfactory. 2. From correlation analyses of extraction items, a degree of concentration on art works was found to most 'affect the viewing satisfactions of art museums. 3. From factor analyses, extraction items were reduced to 11 extraction factors, and a simple extraction structure affecting the viewing satisfactions in art museums. 4. From multiple regression analyses, a extraction factors were derived, and a relative hierarchy among the factors was found.

• Journal of Family Relations
• /
• v.20 no.4
• /
• pp.3-24
• /
• 2016

Objectives: The main purpose of this study was to examine how family strengths affect friend attachment and psychological well-being among college students and to analyze the relative influence of these two variables on psychological well-being. Method: Data were collected by self-administered questionnaire method from 362 university students in four different regions. The data were analyzed through various statistical methods such as t and F tests, Pearson's correlation analyses, and multiple regression analyses. Results: First, there were significant differences in the level of psychological well-being according to gender and the monthly family income, showing that males and higher family income group reported greater psychogocal-well-being level. Males also reported lower level of anxious attachment. Second, correlational analyses results indicated that college students' psychological well-being was positively related with family strengths and secure attachment, and was negatively correlated with avoidant and anxious attachment. Finally, the results of hierarchical multiple regression analyses indicated that college student's psychological well-being was influenced by family communication, secure attachment, and anxious attachment, showing that anxious attachment was the most influential variable. Conclusions: This study suggests the importance of providing education and/or counseling services focusing on strengthening the positive relationship with their friends and on increasing the family communication for college students' psychological well-being.

• Physical Therapy Rehabilitation Science
• /
• v.7 no.1
• /
• pp.29-34
• /
• 2018

Objective: This study aimed to predict balance and gait abilities with the Trunk Impairment scales (TIS) in persons with stroke. Design: Cross-sectional study. Methods: Sixty-eight participants with stoke were assessed with the TIS, Berg Balance scale (BBS), and Functional Gait Assessment (FGA) by a therapist. To describe of general characteristics, we used descriptive and frequency analyses, and the TIS was used as a predictive variable to determine the BBS. In the simple regression analysis, the TIS was used as a predictive variable for the BBS and FGA, and the TIS and BBS were used as predictive variables to determine the FGA in multiple regression analysis. Results: In the group with a BBS score of >45 for regression equation for predicting BBS score using TIS score, the coefficient of determination ($R^2$) was 0.234, and the $R^2$ was 0.500 in the group with a BBS score of ${\leq}45$. In the group with an FGA score >15 for regression equation for predicting FGA score using TIS score, the $R^2$ was 0.193, and regression equation for predicting FGA score using TIS score, the $R^2$ was 0.181 in the group of FGA score ${\leq}15$. In the group of FGA score >15 for regression equation for predicting FGA score using TIS and BBS score, the $R^2$ was 0.327. In the group of FGA score ${\leq}15$ for regression equation for predicting FGA score using TIS and BBS score, the $R^2$ was 0.316. Conclusions: The TIS scores are insufficient in predicting the FGA and BBS scores in those with higher balance ability, and the BBS and TIS could be used for predicting variables for FGA. However, TIS is a strong predictive variable for persons with stroke who have poor balance ability.

• Communications for Statistical Applications and Methods
• /
• v.29 no.4
• /
• pp.413-420
• /
• 2022

In this study we adapt discrete weibull regression model for clustered count data. Discrete weibull regression model has an attractive feature that it can handle both under and over dispersion data. We analyzed the eighth Korean National Health and Nutrition Examination Survey (KNHANES VIII) from 2019 to assess the factors influencing the 1 month outpatient stay in 17 different regions. We compared the results using clustered discrete Weibull regression model with those of Poisson, negative binomial, generalized Poisson and Conway-maxwell Poisson regression models, which are widely used in count data analyses. The results show that the clustered discrete Weibull regression model using random intercept model gives the best fit. Simulation study is also held to investigate the performance of the clustered discrete weibull model under various dispersion setting and zero inflated probabilities. In this paper it is shown that using a random effect with discrete Weibull regression can flexibly model count data with various dispersion without the risk of making wrong assumptions about the data dispersion.

• Journal of Soil and Groundwater Environment
• /
• v.22 no.2
• /
• pp.26-32
• /
• 2017

Regression analyses were conducted for prediction of benzo(a)pyrene (BaP) and total polycyclic aromatic hydrocarbons (PAHs) in soils. Dimensionless units were applied after each PAH was divided by naphthalene (Nap) for the regression analyses using a previously published Swiss data set or all data sets, including Chinese and Brazilian. A strong correlation was found between BaP/Nap ($R^2=0.95$) or ${\Sigma}PAH/Nap$ ($R^2=0.99$) and Pyr/Nap ratios from the Swiss data set. When the developed prediction equation was applied to other measurements to validate its accuracy, there was great agreement between the data and predicted values. This model could be used as a useful tool for the calculation of average BaP and ΣPAH in specific regions without additional tests.

• Journal of Soil and Groundwater Environment
• /
• v.21 no.4
• /
• pp.30-41
• /
• 2016

The amount of groundwater related data is drastically increasing domestically from various sources since 2000. To justify the more expansive continuation of the data acquisition and to derive valuable implications from the data, continued employments of sophisticated and state-of-the-arts statistical tools in the analyses and predictions are important issue. In the present study, we employed a well established machine learning technique of Gaussian Process Regression (GPR) model in the trend analyses of groundwater level for the long-term change. The major benefit of GPR model is that the model provide not only the future predictions but also the associated uncertainty. In the study, the long-term predictions of groundwater level from the stations of National Groundwater Monitoring Network located within Han River Basin were exemplified as prediction cases based on the GPR model. In addition, a few types of groundwater change patterns were delineated (i.e., increasing, decreasing, and no trend) on the basis of the statistics acquired from GPR analyses. From the study, it was found that the majority of the monitoring stations has decreasing trend while small portion shows increasing or no trend. To further analyze the causes of the trend, the corresponding precipitation data were jointly analyzed by the same method (i.e., GPR). Based on the analyses, the major cause of decreasing trend of groundwater level is attributed to reduction of precipitation rate whereas a few of the stations show weak relationship between the pattern of groundwater level changes and precipitation.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.28 no.3
• /
• pp.61-66
• /
• 2003

In many multiple regression analyses, the “multi-collinearity” problem arises since some independent variables are highly correlated with each other. Practically, the Ridge regression method is often adopted to deal with the problems resulting from multi-collinearity. We propose a better alternative method using iteration to obtain an exact least squares estimator. We prove the solvability of the proposed algorithm mathematically and then compare our method with the traditional one.