• Magazine of the Korean Society of Agricultural Engineers
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• v.26 no.2
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• pp.106-124
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• 1984

Monthly streanflow of watersheds is one of the most important elements for the planning, design, and management of water resources development projects, e.g., determination of storage requirement of reservoirs and control of release-water in lowflow rivers. Modeling of longterm runoff is theoretically based on water-balance analysis for a certain time interval. The effect of the casual factors of rainfall, evaporation, and soil-moisture storage on streamflow might be explained by multiple regression analysis. Using the basic concepts of water-balance and regression analysis, it was possible to develop a generalized model called the Regionalized Regression Model for Monthly Streamflow in Korean Watersheds. Based on model verification, it is felt that the model can be reliably applied to any proposed station in Korean watersheds to estimate monthly streamflow for the planning, design, and management of water resources development projects, especially those involving irrigation. Modeling processes and properties are summarized as follows; 1. From a simplified equation of water-balance on a watershed a regression model for monthly streamflow using the variables of rainfall, pan evaporation, and previous-month streamflow was formulated. 2. The hydrologic response of a watershed was represented lumpedly, qualitatively, and deductively using the regression coefficients of the water-balance regression model. 3. Regionalization was carried out to classify 33 watersheds on the basis of similarity through cluster analysis and resulted in 4 regional groups. 4. Prediction equations for the regional coefficients were derived from the stepwise regression analysis of watershed characteristics. It was also possible to explain geographic influences on streamflow through those prediction equations. 5. A model requiring the simple input of the data for rainfall, pan evaporation, and geographic factors was developed to estimate monthly streamflow at ungaged stations. The results of evaluating the performance of the model generally satisfactory.

• Korean Journal of Human Ecology
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• v.23 no.2
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• pp.251-268
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• 2014

Applying the data from 64 single(26 men and 38 women) and 71 childless married men and women(37 men and 34 women) aged between 30 and 45, this study is to understand their future fertility intention. For this purpose, ideal and real number of children that participants plan to have were compared using paired t-test. Second, demographic variables(sex, age, marital status), child care related variables(thoughts about caring children, child care value), individual characteristics(gender role attitude, relation orientation) and social context variables(perceived economic condition, recognition of low fertility policies) were included in a stepwise regression model to explain expected number of children participants plan to have in the future. Results showed that ideal number of children participants wish to have was significantly higher than real number of children they expect to have in the Korean society. The stepwise regression model explained 35% of the variance of the dependent variable. Among four types of variables, child care related variables most powerfully explained expected number of children study participants plan to have in the future. Finally, age, child care value, gender role attitude, and relation orientation significantly explained expected number of children in the future.

• Journal of Community Nutrition
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• v.2 no.2
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• pp.119-128
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• 2000

Lifestyle and dietary behavior intervention as the primary prevention of lipid disorder seems safe and compatible with other treatments of cardiovascular diseases. Cross-sectional associations between lifestyle factors and dietary behavioral factors with plasma lipid and lipoprotein levels were analyzed in 189 middle-aged men in Suwon, Korea. Overnight fasting plasma levels of total cholesterol, high-density lipoprotein(HDL)-cholesterol, triacylglycerol and glucose were analyzed. Blood pressure and anthropometric data were also measured. Lifestyle factors such as smoking status, alcohol consumption and frequency of physical exercise were evaluated by a self-administered questionnaire. Questions regarding dietary behavior were also asked. The subjects were 43.8%${\pm}$7.9 years old, and 23.8%${\pm}$2.6kg/m$^2$. From stepwise regression analyses, significant correlates with total cholesterol level were body mass index(BMI), alcohol intake(negative), age and coffee drinking(model R$^2$=14.3%). BMI, breakfast-skipping, age, and sleeping hours were significant for triacylglycerol level(model R$^2$=15.8%). BMI, alcohol drinking(negative), age, and coffee drinking were significant for low-density lipoprotein(LDL)(model R$^2$=11.7%). Age(negative), BMI(negative), alcohol drinking, stress level(negative), physical exercise, and cigarette smoking(negative) were significant for high-density lipoprotein(HDL)(model R$^2$=12.1%). From stepwise regression analyses, excluding BMI and age as factors in the model, alcohol intake(negative) and coffee drinking were significantly correlated with total cholesterol level(model R$^2$=4.4%) : breakfast-skipping with triacylglycerol(model R$^2$=3.2%) : alcohol intake (negative) with LDL level(model R$^2$=3.4%) : alcohol intake, physical exercise and stress level(negative) with HDL level(model R$^2$=6.3%). The findings suggest that a healthy daily lifestyle and dietary behavior may have an anti-atherogenic effect by altering plasma lipid and lipoprotein levels in middle-aged Korean men. (J Community Nutrition 2(2) : 119∼128, 2000)

• Korean Journal of Applied Biomechanics
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• v.30 no.1
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• pp.83-91
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• 2020

Objective: The study aimed to develop a functional performance index that evaluates the functional performance of Parkinson's patients, i.e., to integrate biomechanical measurements of walking, balance, muscle strength and tremor, and to use multiple linear regression with stepwise methods to identify the most suitable predictors for the progression of disease. Method: A total of 60 subjects were tested for sub-variables of four factors: walking, balance, isometric strength and hand tremors. Potential independet variables were extracted through correlation analysis of the sub-variables and dependent variables, Hoehn & Yahr scale. And then, a stepwise multiple regression analysis using the potential independent variables was performed to identify predictor of Hoehn & Yahr scale. Results: First, the results of the study showed that physical composition and gait had a relatively more correlated with the progression of the disease, compared to balance and hand tremor. Second, Parkinson's functional performance is characterized by dynamic pattern of walking, such as foot clearance and turning angle (TA) of walking, and a high-explained regression model is completed. Conclusion: The study emphasized the importance of walking variables and body composition in minor pathological features compared to Parkinson's patient's balancing ability and hand tremor. Specifically, it revealed that dynamic walking patterns functionally characterize patients. The results are worth considering when assessing functional performance related to the progression of the disease at the site.

• Journal of Navigation and Port Research
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• v.43 no.1
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• pp.72-78
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• 2019

The second-hand ship market provides immediate access to the freight market for shipping investors. When introducing second-hand vessels, the precise estimate of the price is crucial to the decision-making process because it directly affects the burden of capital cost to investors in the future. Previous studies on the second-hand market have mainly focused on the market efficiency. The number of papers on the estimation of second-hand vessel values is very limited. This study proposes an artificial neural network model that has not been attempted in previous studies. Six factors, freight, new-building price, orderbook, scrap price, age and vessel size, that affect the second-hand ship price were identified through literature review. The employed data is 366 real trading records of Panamax second-hand vessels reported to Clarkson between January 2016 and December 2018. Statistical filtering was carried out through correlation analysis and stepwise regression analysis, and three parameters, which are freight, age and size, were selected. Ten-fold cross validation was used to estimate the hyper-parameters of the artificial neural network model. The result of this study confirmed that the performance of the artificial neural network model is better than that of simple stepwise regression analysis. The application of the statistical verification process and artificial neural network model differentiates this paper from others. In addition, it is expected that a scientific model that satisfies both statistical rationality and accuracy of the results will make a contribution to real-life practices.

• Korean Journal of Exercise Nutrition
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• v.24 no.1
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• pp.9-13
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• 2020

[Purpose] This preliminary study aimed to develop a regression model to estimate the resting metabolic rate (RMR) of young and middle-aged Koreans using various easy-to-measure dependent variables. [Methods] The RMR and the dependent variables for its estimation (e.g. age, height, body mass index, fat-free mass; FFM, fat mass, % body fat, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, and resting heart rate) were measured in 53 young (male n = 18, female n = 16) and middle-aged (male n = 5, female n = 14) healthy adults. Statistical analysis was performed to develop an RMR estimation regression model using the stepwise regression method. [Results] We confirmed that FFM and age were important variables in both the regression models based on the regression coefficients. Mean explanatory power of RMR₁ regression models estimated only by FFM was 66.7% (R²) and 66.0% (adjusted R²), while mean standard errors of estimates (SEE) was 219.85 kcal/day. Additionally, mean explanatory power of RMR₂ regression models developed by FFM and age were 70.0% (R²) and 68.8% (adjusted R²), while the mean SEE was 210.64 kcal/day. There was no significant difference between the measured RMR by the canopy method using a metabolic gas analyzer and the predicted RMR by RMR₁ and RMR₂ equations. [Conclusion] This preliminary study developed a regression model to estimate the RMR of young and middle-age healthy Koreans. The regression model was as follows: RMR₁ = 24.383 × FFM + 634.310, RMR₂ = 23.691 × FFM - 5.745 × age + 852.341.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.10 s.181
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• pp.2581-2588
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• 2000

In previous studies, the dynamic resistance, which was calculated by the process variables measured at the electrode of the welding machine, and the electrode displacement were used for quality exa mination. However, in-process usage of such systems is not effective in systems that include a welding gun attached to a robot. In order to overcome such problems, we obtained and used the process variables from the welding machine timer. This would allow us to estimate real time in -process weld quality. For quality estimation, the features were extracted as factors from the primary dynamic resistance patterns, which were measured in t he welding machine timer. The relationship between the indexes and nugget size of the welds was observed through the regression analysis. Using the analyzed factors, a regression model that could estimate nugget diameter was developed. Two regression equations of the model were suggested depending on the factors, and it was showed that the model developed by stepwise method was effective one for weld quality estimation. The developed estimation model was in good linearity with the nugget diameter obtained through the experimentation.

• ETRI Journal
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• v.21 no.3
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• pp.6-15
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• 1999

Many studies in the software reliability have attempted to develop a model for predicting the faults of a software module because the application of good prediction models provides the optimal resource allocation during the development period. In this paper, we consider the change request data collected from the field test of the software module that incorporate a functional relation between the faults and some software metrics. To this end, we discuss the general aspect if regression method, the problem of multicollinearity and the measures of model evaluation. We consider four possible regression models including two stepwise regression models and two nonlinear models. Four developed models are evaluated with respect to the predictive quality.

• Korean Journal of Exercise Nutrition
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• v.25 no.1
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• pp.23-29
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• 2021

[Purpose] This preliminary study aimed to develop a regression model to estimate the non-exercise activity thermogenesis (NEAT) of Korean adults using various easy-to-measure dependent variables. [Methods] NEAT was measured in 71 healthy adults (male n = 29; female n = 42). Statistical analysis was performed to develop a NEAT estimation regression model using the stepwise regression method. [Results] We confirmed that age^A, weight^B, heart rate (HR)_average^C, weight × HR_average^D, weight × HR_sum^E, systolic blood pressure (SBP) × HR_rest^F, fat mass ÷ height^2G, gender × HR_average^H, and gender × weight × HR_sum^I were important variables in various NEAT activity regression models. There was no significant difference between the measured NEAT values obtained using a metabolic gas analyzer and the predicted NEAT. [Conclusion] This preliminary study developed a regression model to estimate the NEAT in healthy Korean adults. The regression model was as follows: sitting = 1.431 - 0.013 × (A) + 0.00014 × (D) - 0.00005 × (F) + 0.006 × (H); leg jiggling = 1.102 - 0.011 × (A) + 0.013 × (B) + 0.005 × (H); standing = 1.713 - 0.013 × (A) + 0.0000017 × (I); 4.5 km/h walking = 0.864 + 0.035 × (B) + 0.0000041 × (E); 6.0 km/h walking = 4.029 - 0.024 × (C) + 0.00071 × (D); climbing up 1 stair = 1.308 - 0.016 × (A) + 0.00035 × (D) - 0.000085 × (F) - 0.098 × (G); and climbing up 2 stairs = 1.442 - 0.023 × (A) - 0.000093 × (F) - 0.121 × (G) + 0.0000624 × (E).

• Journal of Environmental Health Sciences
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• v.38 no.6
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• pp.550-560
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• 2012

Objectives: QSAR methodology was applied to explain two different sets of acceptable daily intake (ADI) data of 74 pesticides proposed by both the USEPA and WHO in terms of setting guidelines for food and drinking water. Methods: A subset of calculated descriptors was selected from Dragon$^{(R)}$ software. QSARs were then developed utilizing a statistical technique, genetic algorithm-multiple linear regression (GA-MLR). The differences in each specific model in the prediction of the ADI of the pesticides were discussed. Results: The stepwise multiple linear regression analysis resulted in a statistically significant QSAR model with five descriptors. Resultant QSAR models were robust, showing good utility across multiple classes of pesticide compounds. The applicability domain was also defined. The proposed models were robust and satisfactory. Conclusions: The QSAR model could be a feasible and effective tool for predicting ADI and for the comparison of logADIEPA to logADIWHO. The statistical results agree with the fact that USEPA focuses on more subtle endpoints than does WHO.