• Nuclear Engineering and Technology
• /
• v.53 no.12
• /
• pp.4189-4200
• /
• 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 Adult Nursing
• /
• v.17 no.5
• /
• pp.772-782
• /
• 2005

Objectives: The purpose of this study was to investigate the relationship between Korean lifestyle characteristics and health status and to identify the variables influencing health in Korea. Methods: A cross-sectional descriptive correlational design was used to explore the lifestyle characteristics and health status of 397 Korean adults. Correlational analysis calculated the correlation between lifestyle and health status. To examine the relationship among demographic characteristics, lifestyle, and health status we used the t-test and one-way ANOVA. Stepwise multiple regression was conducted to examine the significant predictors of general health among subjects. Results: Positive correlations were seen between general health (GH) and the overall score and subscales of the Lifestyle. The stepwise regression model showed that vitality (VA), body pain (BP), nutrition, and occupation were significant variables influencing general health (GH). Conclusions: These findings provide evidence regarding the lifestyle patterns and healthstatus among Koreans. When planning intervention strategies for this population, exercise and physical activity should be principal focus areas.

• Korean Journal of Social Welfare
• /
• v.45
• /
• pp.288-314
• /
• 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 Korean Journal of Applied Statistics
• /
• v.18 no.2
• /
• pp.435-443
• /
• 2005

Coefficients of determination in logistic regression analysis are defined as various statistics, and their values are relatively smaller than those for linear regression model. These coefficients of determination are not generally used to evaluate and diagnose logistic regression model. Liao and McGee (2003) proposed two adjusted coefficients of determination which are robust at the addition of inappropriate predictors and the variation of sample size. In this work, these adjusted coefficients of determination are applied to variable selection method for logistic regression model and compared with results of other methods such as the forward selection, backward elimination, stepwise selection, and AIC statistic.

• Journal of Korea Water Resources Association
• /
• v.51 no.8
• /
• pp.677-686
• /
• 2018

There remain numerous ungauged watersheds in Korea owing to limited spatial and temporal streamflow data with which to estimate hydrological model parameters. To deal with this problem, various regionalization approaches have been proposed over the last several decades. However, the results of the regionalization models differ according to climatic conditions and regional physical characteristics, and the results of the regionalization models in previous studies are generally inconclusive. Thus, to improve the performance of the regionalization methods, this study attaches hydrological model parameters obtained using a spatial proximity model to the explanatory variables of a regional regression model and defines it as a hybrid regionalization model (hybrid model). The performance results of the hybrid model are compared with those of existing methods for 37 test watersheds in South Korea. The GR4J model parameters in the gauged watersheds are estimated using a shuffled complex evolution algorithm. The variation inflation factor is used to consider the multicollinearity of watershed characteristics, and then stepwise regression is performed to select the optimum explanatory variables for the regression model. Analysis of the results reveals that the highest modeling accuracy is achieved using the hybrid model on RMSE overall the test watersheds. Consequently, it can be concluded that the hybrid model can be used as an alternative approach for modeling ungauged watersheds.

• Journal of Korean Society of Transportation
• /
• v.10 no.2
• /
• pp.25-42
• /
• 1992

The purpose of this paper is to construct a model to compute a progression adjustment factor on a signalized network. In a way to construct the model, a simulation method is introduced and the TRAF-NETSIM is used as a tool of simulation. The structure of the network chooses an urban arterial network so as to measure the effect of progression and compute average stopped delay on each link. A regression model is constructed by using the results of the simulation. The stepwise variable selection in the regression model in used. The findings of this paper are as follows: i)The secondary queue and platoon ratio are sensitive to the values of the progression adjustment factor ii) The continuous model can practically reflect on various situations in the real world. The platoon adjustment factor can be computed by this model and the data required for this model can be easily obtained in the field.

• Korean Journal of Exercise Nutrition
• /
• v.25 no.1
• /
• pp.35-41
• /
• 2021

[Purpose] This pilot study aimed to develop a regression model to estimate the excess post-exercise oxygen consumption (EPOC) of Korean adults using various easy-to-measure dependent variables. [Methods] The EPOC and dependent variables for its estimation (e.g., sex, age, height, weight, body mass index, fat-free mass [FFM], fat mass, % body fat, and heart rate_sum [HR_sum]) were measured in 75 healthy adults (31 males, 44 females). Statistical analysis was performed to develop an EPOC estimation regression model using the stepwise regression method. [Results] We confirmed that FFM and HR_sum were important variables in the EPOC regression models of various exercise types. The explanatory power and standard errors of estimates (SEE) for EPOC of each exercise type were as follows: the continuous exercise (CEx) regression model was 86.3% (R²) and 85.9% (adjusted R²), and the mean SEE was 11.73 kcal, interval exercise (IEx) regression model was 83.1% (R²) and 82.6% (adjusted R²), while the mean SEE was 13.68 kcal, and the accumulation of short-duration exercise (AEx) regression models was 91.3% (R²) and 91.0% (adjusted R²), while the mean SEE was 27.71 kcal. There was no significant difference between the measured EPOC using a metabolic gas analyzer and the predicted EPOC for each exercise type. [Conclusion] This pilot study developed a regression model to estimate EPOC in healthy Korean adults. The regression model was as follows: CEx = -37.128 + 1.003 × (FFM) + 0.016 × (HR_sum), IEx = -49.265 + 1.442 × (FFM) + 0.013 × (HR_sum), and AEx = -100.942 + 2.209 × (FFM) + 0.020 × (HR_sum).

• Journal of the Korean Home Economics Association
• /
• v.28 no.1
• /
• pp.57-66
• /
• 1990

The purpose of this study was to investigate the analytical model of the implicit price according to objective and subjective characteristics of housing. The hedonic price regression was used for estimating the implicit price. The subjectives of this study were 1,143 dwellers who live in Seoul metropolitan area. Taejeon, and Jeonju. Satistical analyses were conducted using frequencies, percentiles, mean, and multiple regression. The major findings were as follows: 1. There was a significant difference in the implict price of the apartment between owners and renters. 2. There was a sginificant difference in the implicit price of the apartment among Seoul metropolitan area, Taejeon, and Jeonju. 3. Using a stepwise multiple regression method, the order of variables as they were entered in the model were different between tenure types (owner/renter), and regions(Seoul metroplitan area/Taejeon/Jeonju). 4. The linear model was the most appropriate noe which explained the housing price. 5. Subjective characteristics of housing in Taejeon and Jeonju had an effect on the housing price more than those in Seoul metropolitan area.

• Journal of the Korean Home Economics Association
• /
• v.35 no.6
• /
• pp.87-99
• /
• 1997

This study intends to find the effects of children's stress level and coping behaviors on their stress symptoms. The subjects were 840 4-6th grade children in Seoul. The data were analyzed by frequencies, percentages, means, ANOVA, stepwise regression and Cronbach's α. The regression model explained 46% of children's stress symptoms which were affected by coping behaviors(emotional aggression, positive revaluation, seperation for emotional relaxation) and by stressors(children's social-life, individual factors, school-life).

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.32 no.1B
• /
• pp.9-20
• /
• 2012

Recently to overcome limitations of conceptual, hydrological and physics based models for flood stage forecasting, multiple linear regression model as one of data-driven models have been widely adopted for forecasting flood streamflow(stage). The objectives of this study are to compare performance of different multiple linear regression models according to regression coefficient estimation methods and determine most effective multiple linear regression flood stage forecasting models. To do this, the time scale was determined through the autocorrelation analysis of input data and different flood stage forecasting models developed using regression coefficient estimation methods such as LS(least square), WLS(weighted least square), SPW(stepwise) was applied to flood events in Jungrang stream. To evaluate performance of established models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient (NSEC), mean absolute error (MAE), adjusted coefficient of determination($R^{*2}$). The results show that the flood stage forecasting model using SPW(stepwise) parameter estimation can carry out the river flood stage prediction better in comparison with others, and the flood stage forecasting model using LS(least square) parameter estimation is also found to be slightly better than the flood stage forecasting model using WLS(weighted least square) parameter estimation.