• Journal of Preventive Medicine and Public Health
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• v.36 no.1
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• pp.85-91
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• 2003

Objectives : We investigated the effects of the economic growth and unemployment rates on the suicide rate in Korea, between 1983 and 2000, using a time-series regression model. The purpose of this study was to model and test the magnitude of the rate of suicide, with the Korean unemployment rate and GDP. Methods : Using suicide rate per 100,000 Koreans and the unemployment rates between 1983 and 2000, as published by the Korea National Statistical Office, and the rate of fluctuation of the Korean GDP (Gross Domestic Product), as provided by the Bank of Korea, as an index of the economic growth rate, a time-series regression analysis, with a first-order autoregressive regression model, was peformed. Results : An 81.5% of the variability in the suicide rate was explained by GDP, and 82.6% Of that was explained by the unemployment rate. It was also observed that the GDP negatively correlated with the suicide rate, while the unemployment and suicide rates were positively correlated. For subjects aged over 20, both the GDP and unemployment rate were found to be a significant factors in explaining suicide rates, with coefficients of determination of 86.5 and 87.9%, respectively. For subjects aged under 20, however, only the GDP was found to be a significant factor in explaning suicide rates (the coefficient of determination is 38.4%). Conclusion : It was found that the suicide rate was closely related to the National's economic status of Korea, which is similar to the results found in studies in other countries. We expected, therefore, that this study could be used as the basis for further suicide-related studies.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.6
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• pp.637-644
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• 2010

The purpose of this study is to analyze characteristics of water quality factor scientifically and develop the multiple regression model predicting dissolved oxygen to save periodic replacement costs for dissolved oxygen sensor. Correlation analysis using the environmental data obtained from 2 different land-based fish farms of the Geogeum-do, Geheung-gun coastal area during the periods from November 2008 to January 2009 shows that water temperature was negatively correlated with dissolved oxygen and pH butpH was positively correlated with salinity and dissolved oxygen. The information of Keumho fish farm in 2009 is presented by the tables which are monthly statistics of water quality factors and seasonable difference by the Duncan's post-test. Also we developed multiple regression model predicting dissolved oxygen, the usefulness of which was verified by the comparison graph between estimates and actual observations. The developed regression model shows that seawater temperature and salinity give negative affect to dissolved oxygen while pH gives positive affect to it. Lastly the seawater temperature has much higher explanatory power than pH factor.

• Journal of Information Technology Services
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• v.6 no.2
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• pp.63-80
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• 2007

In this paper, the function factors contributing to ITA/EA functions in their organizations, are suggested. Also the reasons to the construction of IT governance system and their effects on the their organizations are discussed and the relationships are verified by empirical model. From the survey, the data of the 227 respondents were collected and regression analysis was performed for validating the research model. ITA/EA functions consist of the IT infrastructure systemization, ITA/EA business process support, IT investment efficiency factors. And the factors of IT resource and performance management, IT process management, IT service management are included to IT governance. The main analysis results described significantly are shows as follows. At first, IT resource and performance management factor is effected by the ITA/EA business process support factor and IT investment efficiency factor. In similarly, IT service management factor is also affected by the factors such as ITA/EA business process support, IT investment efficiency, and IT infrastructure systemization. In additional analysis, IT investment efficiency factor in official sectors, ITA/EA business process support factor in private sectors respectively are described as the significant factors on the IT governance.

• Bulletin of the Society of Naval Architects of Korea
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• v.26 no.4
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• pp.14-26
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• 1989

This paper describes the method of statistical analysis and its programs for predicting the ship's powering performance. The equation for the wavemaking resistance coefficient is derived as the sectional area coefficients by using the wavemaking resistance theory and its regression coefficients are determined from the regression analysis of the model test results. The equations for the form factor, wake franction and thrust deduction fraction are derived by purely regression analysis of the principal dimensions, sectional area coefficients and model test results. The statistical analyses are performed using the various descriptive statistic and stepwise regression analysis techniques. The powering performance prognosis program is developed to cover the prediction of resistance coefficients, propulsive coefficients, propeller open-water efficiency and various scale effect corrections.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.847-859
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• 2011

cDNA microarray-based comparative genomic hybridization(CGH) data includes low-intensity spots and thus a statistical strategy is needed to detect subtle differences between different cancer classes. In this study, genes displaying a high frequency of alteration in one of the different classes were selected among the pre-selected genes that show relatively large variations between genes compared to total variations. Utilizing copy-number changes of the selected genes, this study suggests a statistical approach to predict patients' classes with increased performance by pre-classifying patients with similar genetic alteration scores. Two-stage logistic regression model(TLRM) was suggested to pre-classify homogeneous patients and predict patients' classes for cancer prediction; a decision tree(DT) was combined with logistic regression on the set of informative genes. TLRM was constructed in cDNA microarray-based CGH data from the Cancer Metastasis Research Center(CMRC) at Yonsei University; it predicted the patients' clinical diagnoses with perfect matches (except for one patient among the high-risk and low-risk classified patients where the performance of predictions is critical due to the high sensitivity and specificity requirements for clinical treatments. Accuracy validated by leave-one-out cross-validation(LOOCV) was 83.3% while other classification methods of CART and DT performed as comparisons showed worse performances than TLRM.

• Nuclear Engineering and Technology
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• v.42 no.2
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• pp.163-174
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• 2010

As pointed out in the OECD BEMUSE Program, when a high computation time is taken to obtain the relevant output values of a complex physical model (or code), the number of statistical samples that must be evaluated through it is a critical factor for the sampling-based uncertainty analysis. Two alternative methods have been utilized to avoid the problem associated with the size of these statistical samples: one is based on Wilks' formula, which is based on simple random sampling, and the other is based on the conventional nonlinear regression approach. While both approaches provide a useful means for drawing conclusions on the resultant uncertainty with a limited number of code runs, there are also some unique corresponding limitations. For example, a conclusion based on the Wilks' formula can be highly affected by the sampled values themselves, while the conventional regression approach requires an a priori estimate on the functional forms of a regression model. The main objective of this paper is to assess the feasibility of the ACE-RSM approach as a complementary method to the Wilks' formula and the conventional regression-based uncertainty analysis. This feasibility was assessed through a practical application of the ACE-RSM approach to the LOFT L2-5 LBLOCA PCT uncertainty analysis, which was implemented as a part of the OECD BEMUSE Phase III program.

• Journal of Applied Reliability
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• v.16 no.2
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• pp.98-103
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• 2016

Purpose: The purpose of this paper is to get a meaningful information for improving manufacturing quality of the products before they are produced in client's manufacturing process. Methods: A variety of data mining techniques have been being used for wide range of industries from process data in manufacturing factories for quality improvement. One application of those is to get meaningful information from process data in manufacturing factories for quality improvement. In this paper, the failure rate at client's manufacturing process is predicted by using the parameters of the characteristics of the product based on PCA (Principle Component Analysis) and regression analysis. Results: Through a case study, we proposed the predicting methodology and regression model. The proposed model is verified through comparing the failure rates of actual data and the estimated value. Conclusion: This study can provide the guidance for predicting the failure rate on the manufacturing process. And the manufacturers can prevent the defects by confirming the factor which affects the failure rate.

• Journal of Power System Engineering
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• v.13 no.5
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• pp.76-81
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• 2009

The fatalities of pedestrian account for about 21.2% of all fatalities at 2007 year in Korea. To reconstruct exactly the accident, it is important to calculate the throw distance of pedestrian in car to pedestrian accident. The frontal shape of SUV vehicle is dissimilar to passenger car and bus, so the trajectory and throw distance of pedestrian by SUV vehicle is not the same of passenger car and bus. The influencing on it can be classified into the factors of vehicle and pedestrian, and road factor. It was analyzed by PC-CRASH for simulation, and SPSS s/w was used for regression analysis. From the simulation results, the maximum impact energy of multi-body of pedestrian was occurred to that of torso body at the same time. And the throw distance increased with the increasing of impact velocity, and decreased with the increasing of impact offset. Also it decreased with the increasing of velocity of pedestrian at accident, and the throw distance of wet road was longer than that of dry road. Finally, the regression analysis model of SUV(Nissan Pathfinder type)vehicle in car to pedestrian accident was as follows; $$disti_i=-0.87-0.11offseti_i+0.69speed_i-4.27height_i+0.004walk_i+0.63wet_i+{\epsilon}_i$$.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05b
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• pp.1334-1339
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• 2002

The objective of this study is to help practicing engineers easily use the Clark model which is used for estimating the magnitude of design flood for small stream. A representative unit hydrograph was derived on the basis of the past rainfall-runoff data and unit hydrographs, and the storage coefficient of Clark model was estimated by using hydrograph recession analysis. Since the storage coefficient(K) is a dominating factor among the parameters of Clark method, a mulitple regression formula, which has the drainage area, main channel length and slope as parameters, is propsed to estimate K value of a basin where measured data are missing. The result of regression analysis showed that there is a correlation between a storage coefficient(K) and aforemetioned three parameters in homogenious basins. A regression formular for K was derived using these correlations in a basin of Han River, Nakdong River, Young River, Kum River and Sumjin River

• Journal of Preventive Veterinary Medicine
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• v.41 no.3
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• pp.129-132
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• 2017

The current study identified risk factors associated with porcine circovirus type 2 (PCV2) infection on pig farms in the Republic of Korea using a multinomial logistic regression model to evaluate the PCV2 infection status of pigs at different growth stages. Compulsory disinfection of visitors (odds ratio [OR]: 0.019, 95% confidence interval [CI]: <0.001-0.378, p=0.0095), compulsory registration of visitors (OR: 0.002, 95% CI: <0.001-0.184, p=0.0070), regular blood testing (OR: 0.012, 95% CI: <0.001-0.157, p=0.0007), and running on-farm biosecurity learning programs for workers (OR: 0.156, 95% CI: 0.040-0.604, p=0.0072 and OR: 0.201, 95% CI: 0.055-0.737, p=0.0155, respectively) were identified as factors which could reduce the risk of PCV2 infection. However, visitation by a regular veterinarian (OR: 32.733, 95% CI: 3.768-284.327, p=0.0016) was associated with PCV2 infection.