• Journal of Korea Water Resources Association
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• v.41 no.1
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• pp.17-25
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• 2008

Whether rating curves are used in practice or new ones are derived, the characteristics of regression analysis are often neglected. For example, a discharge rating curve, which is established from a regression of observed water levels (H) on observed flowrates(Q), is sometimes used for estimating a design water level corresponding to a simulated design flood runoff. However, if independent and dependent variables are changed with each other, the regression equation is changed in existing regression analysis, which is derived from vertical errors between observed data and regression line. Thus, regression equations should not be applied inversely. To avoid this problem, A new two-way variable least-squares regression analysis is proposed. The new method was applied to the rating curves of five water level stations on main stream of Nakdong River. The three kinds of regression models, which are respectively regression of Q versus H (model 1), H versus Q (model 2) and two-way (model 3), showed that the new method can reduce inadvertent mistakes when applied in practice.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.12
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• pp.5037-5041
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• 2015

Background: An hCG regression curve has been used to predict the natural history and response to chemotherapy in gestational trophoblastic disease. We constructed hCG regression curves in high-risk gestational trophoblastic neoplasia (GTN) treated with EMA/CO and identified an optimal hCG level to detect EMA/CO resistance in GTN. Materials and Methods: Eighty-one women with GTN treated with EMA/CO were classified as primary high-risk GTN (n = 65) and single agent-resistance GTN (n = 16). The hCG levels prior to each course of chemotherapy were plotted in the 10th, 50th, and 90th percentiles to construct the hCG regression curves. Diagnostic performance was evaluated for an optimal cut-off value. Results: The median hCG levels were 264,482 mIU/mL mIU/mL and 495.5 mIU/mL mIU/mL for primary high-risk GTN and single agent-resistance GTN, respectively. The 50th percentile of the hCG level in primary high-risk GTN and single agent-resistance turned to normal before the 4th and the 2nd course of chemotherapy, respectively. The 90th percentile of the hCG level in primary high-risk GTN and single agent-resistance turned to normal before the 9th and the 2nd course of chemotherapy, respectively. The hCG level of ${\geq}118.6mIU/mL$ mIU/mL at the 5thcourse of EMA/CO predicted the EMA/CO resistance in primary high-risk GTN patients with a sensitivity of 85.7% and a specificity of 100%. Conclusion: EMA/CO resistance in primary high-risk GTN can be predicted by using an hCG regression curve in combination with the cut-off value of 118.6 mIU/mL at the 5thcourse of chemotherapy.

• Explosives and Blasting
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• v.24 no.1
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• pp.9-19
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• 2006

The objective of this study is to propose curve fitted equations that can facilitate calculations and improve a practical applicability when the seismic performance of buried pipelines needs to be evaluated. The curve fitted equations are derived based on the evaluation of the dynamic responses of concrete pipe with a boundary condition of fixed-free ends. To study the dynamic response of underground pipe, the numerical analysis program developed in the previous research has been used. The location of maximum strain has been determined through dynamic analyses for a boundary condition of fixed-free ends. Then $wavelength{\lambda}$ of 5-1000(m) and propagation velocity(Vs) of 100-2000(m/s) have been applied at the location of maximum strain and the unit srain curve with the changes of the $wavelength{\lambda}$ and propagation velocity(Vs) has been obtaind. Non-linear least-square regression has been used to develop highly applicable curve fitted equations and various types of exponential regression equations have been checked out. Thus curve fitted equations and necessary coefficients with best results are suggested.

• Magazine of the Korean Society of Agricultural Engineers
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• v.37 no.5
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• pp.73-80
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• 1995

The uses of stage-storage curve function are diverse in irrigation reservoirs. The curve functions would be used to determine the optimal size of spillway length and the inundation area above full water level based on the flood routing in reservoirs. In addition, the curve function would he used to transform the stage to the storage for the reservoir water management, in which the storage is the supply water. Besides those, the curve is necessary for the planning of dredging, the estimation of the effective and the dead storage, the drought management by reservoir, etc. The curve function data, however, are almost unavailable for these purposes. According to the statistics, about 74% of the 2, 900 resevoirs which are maintained by Farm Land Improvement Association have no more effective data. Therefore, the simulation of the curve function could be better alternative. The curve functions were simulated derivating the regression equations based on the basin relief ratio and the effective depth. The results of the verification show the enough reliability of the application to generate the curve function in some reservoirs which do not have the surveyed stage-storage data. Also, even though the averaged curve function would be applicated without the basin relief ratio data, the result shows that the simulated curve is closer to the real one than the linear function by only the existing effective storage data.

• Korean Journal of Clinical Laboratory Science
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• v.41 no.3
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• pp.135-139
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• 2009

Pulmonary function test has been know to be greatly affected by body indices, such as sex, age, height, body weight, body surface area (BSA) and body mass index (BMI), so hat this study was focused to see the relationship between body index and flow-volume curves. Subjects were 156 (male 90, female 66) and they were examined for pulmonary function test in terms of body index and correlation/multiple regression analysis of flow-volume curves at Presbyterian Medical Center from March to August, 2009. The followings results after analyzing the correlation between body index and flow-volume curves. Although flow-volume curve FEF25-75% showed close correlation with age, body weight, and body surface area, but not with body mass index. In addition, multiple regression analysis was performed to see how each body index affects flow-volume curve FEF25-75%, and FEF25-75% dispersion was explained as 74.5% with age only, 94.2% with age and height, and 96% with age, height, and sex. Therefore, sex, age and height that are mainly used for predictive formular of pulmonary function test and nomogram were important factors for pulmonary function test itself, and further study must be done for other body index.

• Journal of Korean Society of Transportation
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• v.10 no.3
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• pp.103-122
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• 1992

The objective of this research is to develop a traffic accident forecasting model using traffic accident data in pusan from 1963 to 1991 and then to make short-term forecasts('93~'94) of traffic accidents in pusan. In this research, several forecasting models are developed. They include a multiple regression model, a time-series ARIMA model, a Logistic curve model, and a Gompertz curve model. Among them, the model which shows the most significance in forecasting accuracy is selected as the traffic accident forecasting model. The results of this research are as followings. 1. The existing model such as Smeed model which was developed for foreign countries shows only 47.8% explanation for traffic accident deaths in Korea. 2. A nonliner regression model ($R^2$=0.9432) and a Logistic curve model are appeared to be th gest forecasting models for the number of traffic accidents, and a Logistic curve model shows th most significance in predicting the accident deaths and injuries. 3. The forecasting figures of the traffic accidents in pusan are as followings: . In 1993, 31, 180 accidents are predicted to happen, and 430 persons are predicted to be deaths and 29, 680 persons are predicated to be injuries. . In 1994, 33, 710 accidents are predicted to happen, and 431.persons are predicted to be deat! and 30, 510 persons are predicted to be injuried. Therefore, preventive measures against traffic accidents are certainly required.

• Journal of Korea Water Resources Association
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• v.41 no.9
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• pp.943-958
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• 2008

This study employs Bayesian regression analysis for fitting discharge rating curves. The parameter estimates using the Bayesian regression analysis were compared to ordinary least square method using the t-distribution. In these comparisons, the mean values from the t-distribution and the Bayesian regression are not significantly different. However, the difference between upper and lower limits are remarkably reduced with the Bayesian regression. Therefore, from the point of view of uncertainty analysis, the Bayesian regression is more attractive than the conventional method based on a t-distribution because the data size at the site of interest is typically insufficient to estimate the parameters in rating curve. The merits and demerits of the two types of estimation methods are analyzed through the statistical simulation considering heteroscedasticity. The validation of the Bayesian regression is also performed using real stage-discharge data which were observed at 5 gauges on the Anyangcheon basin. Because the true parameters at 5 gauges are unknown, the quantitative accuracy of the Bayesian regression can not be assessed. However, it can be suggested that the uncertainty in rating curves at 5 gauges be reduced by Bayesian regression.

• Journal of The Korean Society of Integrative Medicine
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• v.10 no.3
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• pp.107-117
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• 2022

Purpose : The purpose of this study was to establish a model of the predictive factors for success or failure of examinees undertaking the Korean physical therapist licensing examination (KPTLE). Additionally, we assessed the pass/fail cut-off point. Methods : We analyzed the results of 10,881 examinees who undertook the KPTLE, using data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was the test result (pass or fail), and the input variables were: sex, age, test subject, and total score. Frequency analysis, chi-square test, descriptive statistics, independent t-test, correlation analysis, binary logistic regression, and receiver operating characteristic (ROC) curve analyses were performed on the data. Results : Sex and age were not significant predictors of attaining a pass (p>.05). The test subjects with the highest probability of passing were, in order, medical regulation (MR) (Odds ratio (OR)=2.91, p<.001), foundations of physical therapy (FPT) (OR=2.86, p<.001), diagnosis and evaluation for physical therapy (DEPT) (OR=2.74, p<.001), physical therapy intervention (PTI) (OR=2.66, p<.001), and practical examination (PE) (OR=1.24, p<.001). The cut-off points for each subject were: FPT, 32.50; DEPT, 29.50; PTI, 44.50; MR, 14.50; and PE, 50.50. The total score (TS) was 164.50. The sensitivity, specificity, and the classification accuracy of the prediction model was 99 %, 98 %, and 99 %, respectively, indicating high accuracy. Area under the curve (AUC) values for each subject were: FPT, .958; DEPT, .968; PTI, .984; MR, .885; PE, .962; and TS, .998, indicating a high degree of fit. Conclusion : In our study, the predictive factors for passing KPTLE were identified, and the optimal cut-off point was calculated for each subject. Logistic regression was adequate to explain the predictive model. These results will provide universities and examinees with useful information for predicting their success or failure in the KPTLE.

• Journal of The Geomorphological Association of Korea
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• v.27 no.2
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• pp.65-80
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• 2020

This paper deals with risk assessment of life in a landslide-prone area by a GIS-based modeling method. Landslide susceptibility maps can provide a probability of landslide prone areas to mitigate or proper control this problems and to take any development plan and disaster management. A landslide inventory map of the study area was prepared based on past historical information and aerial photography analysis. A total of 550 landslides have been counted at the whole study area. The extracted landslides were randomly selected and divided into two different groups, 50% of the landslides were used for model calibration and the other were used for validation purpose. Eleven causative factors (continuous and thematic) such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in hazard analysis. The correlation between landslides and these factors, pixels were divided into several classes and frequency ratio was also extracted. Eventually, a landslide susceptibility map was constructed using a logistic regression model based on entire events. Moreover, the landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract a success rate curve. Based on the results, logistic regression produced an 85.18% accuracy, so we believed that the model was reliable and acceptable for the landslide susceptibility analysis on the study area. In addition, for risk assessment, vulnerability scale were added for social thematic data layer. The study area predictive landslide affected pixels 2,000 and 5,000 were also calculated for making a probability table. In final calculation, the 2,000 predictive landslide affected pixels were assumed to run. The total population causalities were estimated as 7.75 person that was relatively close to the actual number published in Korean Annual Disaster Report, 2006.

• Korean Journal of Biological Psychiatry
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• v.12 no.2
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• pp.165-172
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• 2005

Objectives:The purpose of this study is to 1) conduct a discrimination analysis of schizophrenia and bipolar affective disorder using MMPI profile through artificial neural network analysis and logistic regression analysis, 2) to make a comparison between advantages and disadvantages of the two methods, and 3) to demonstrate the usefulness of artificial neural network analysis of psychiatric data. Procedure:The MMPI profiles for 181 schizophrenia and bipolar affective disorder patients were selected. Of these profiles, 50 were randomly placed in the learning group and the remaining 131 were placed in the validation group. The artificial neural network was trained using the profiles of the learning group and the 131 profiles of the validation group were analyzed. A logistic regression analysis was then conducted in a similar manner. The results of the two analyses were compared and contrasted using sensitivity, specificity, ROC curves, and kappa index. Results:Logistic regression analysis and artificial neural network analysis both exhibited satisfactory discriminating ability at Kappa index of greater than 0.4. The comparison of the two methods revealed artificial neural network analysis is superior to logistic regression analysis in its discriminating capacity, displaying higher values of Kappa index, specificity, and AUC(Area Under the Curve) of ROC curve than those of logistic regression analysis. Conclusion:Artificial neural network analysis is a new tool whose frequency of use has been increasing for its superiority in nonlinear applications. However, it does possess insufficiencies such as difficulties in understanding the relationship between dependent and independent variables. Nevertheless, when used in conjunction with other analysis tools which supplement it, such as the logistic regression analysis, it may serve as a powerful tool for psychiatric data analysis.