• Journal of Preventive Medicine and Public Health
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• 제36권2호
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• pp.125-130
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• 2003

Objectives : This study evaluated the stress of community residents using the General Health Questionnaire, GHQ-60, as an instrument of stress measurement. Methods : The study included 2100 residents, aged 20 and over, living in three areas, a large city, a medium sized city and a rural area, between June and September 2001. A questionnaire interviewing method was used to collect data. The data were analyzed using a t-test, ANOVA, Pearson's correlation coefficients and multiple regression analysis. Results : In this study, the degree of stress, as measured by the GHQ-60, was shown to be significantly higher in the following categories: females, people over 60 years old, people engaged in the primary industries and labor work, low incomes, the divorced and the bereaved, people who received no more than an elementary education, people who suffer from chronic diseases and non-exercisers. A factor analysis suggested that there were three factors of social dysfunction factors; psychosomatic symptom, and depression and anxiety, The social dysfunction factors was statistically significant for the groups described above. The factor of psychosomatic symptoms was statistically significant in the rural residents, and in the groups describedabove. The depression and anxiety factor was statistically significant in the large city residents, people aged between 20-29 years, students, unmarried persons, university graduates and those having suffered from chronic diseases. From the multiple linear regression analyses, chronic disease, exercise, gender and income, proved to be significant stress related factors Conclusions : This study suggests that special attention should be given to the management of the chronic invalided, non-exercisers, females and snail income earners, in order to maintain and promote the psychological health of residents in a community.

• Annals of Occupational and Environmental Medicine
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• 제35권
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• pp.51.1-51.15
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• 2023

Background: Recently, there has been increasing worldwide concern about outdoor air pollution, especially particulate matter (PM), which has been extensively researched for its harmful effects on the respiratory system. However, sufficient research on its effects on cardiovascular diseases, such as hypertension, remains lacking. In this study, we examine the associations between PM levels and hypertension and hypothesize that higher PM concentrations are associated with elevated blood pressure. Methods: A total of 133,935 adults aged ≥ 40 years who participated in the Korean Genome and Epidemiology Study were analyzed. Multiple linear regression analyses were conducted to investigate the short- (1-14 days), medium- (1 and 3 months), and long-term (1 and 2 years) impacts of PM on blood pressure. Logistic regression analyses were conducted to evaluate the medium- and long-term effects of PM on blood pressure elevation after adjusting for sex, age, body mass index, health-related lifestyle behaviors, and geographic areas. Results: Using multiple linear regression analyses, both crude and adjusted models generated positive estimates, indicating an association with increased blood pressure, with all results being statistically significant, with the exception of PM levels over the long-term period (1 and 2 years) in non-hypertensive participants. In the logistic regression analyses on non-hypertensive participants, moderate PM₁₀ (particulate matter with diameters < 10 ㎛) and PM_2.5 (particulate matter with diameters < 2.5 ㎛) levels over the long-term period and all high PM₁₀ and PM_2.5 levels were statistically significant after adjusting for various covariates. Notably, high PM_2.5 levels of the 1 year exhibited the highest odds ratio of 1.23 (95% confidence interval: 1.19-1.28) after adjustment. Conclusions: These findings suggest that both short- and long-term exposure to PM is associated with blood pressure elevation.

• 대한교통학회지
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• 제5권2호
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• pp.55-72
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• 1987

The work analyzes the existing situation and defines special problems concerning traffic accidents in the two countries. The report is divided into three parts: 1) Using the global approach of SMEED, the data were evaluated using multiple regression analysis, and homogeneous groups of countries were defined by cluster analysis. In the global approach, the linear model is better than SMEED's non-linear model in explaining the number of fatalities. Among the different groups of countries, the linear approach was found to be better suited for industrialized countries and the non-linear approach better for the developing countries. T도 comparison of traffic fatality data for the Federal Republic the developing countries. The comparison of traffic fatality data for the Federal Republic of Germany and the Republic of Korea showed different regression equations during the same time period. 2) The BOX/JENKINS time series analysis on a monthly basis points out clearly similar seasonal patterns for the two countries over the years studied. The decrease in traffic accidents following the intensification of the safety belt requirement was proved in the ARIMA model. It amounts to 7 to 8 percent fewer personal injury accidents and fatal accidents. The identified increase in safety in the Federal Republic of Germany since the 1970s is mainly due to the reduction of accident severity in residential areas. 3) Speeds and headways on motorways in th3e two countries were also compared. The measurements point out that German road users drive faster, take more risks, and accept shorter time gaps than Korean road users. However, the accident statistics show accident rates for Korea that are several times higher than those in the Federal Republic of Germany.

• The Journal of Asian Finance, Economics and Business
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• 제8권2호
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• pp.1017-1023
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• 2021

This research aims to determine the effect of customers' threat emotion and price on the decision to purchase a certain smartphone product. This study uses a quantitative method with a type of descriptive and causal research. It employs non-probability sampling with purposive sampling, with 385 respondents to answer the questionnaires. Data analysis techniques used descriptive analysis and multiple linear regression analysis. Based on the results of descriptive analysis of emotion, price and purchasing decisions are in sync with each other. The results of multiple linear regression analysis techniques indicate the threat emotion and brand trust are influential against the positive decision to purchase smartphone products. The magnitude of the influence of emotions and price have simultaneous effect on purchasing decisions and other decision variables, which are not included in this study, also play minor role in determining purchase intention, such as product quality, brand image and others. Partially, threat emotion and brand trust have a positive effect toward purchasing decisions. The magnitude of the highest influence was the one of price, then followed by emotional threats. The findings of this study suggest that psychological and behavioral effects also play important roles in determining customers' purchase decision.

• 한국산학기술학회논문지
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• 제16권12호
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• pp.8791-8799
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• 2015

본 논문에서는 편도(One-way)카셰어링 시스템을 중심으로 이벤트를 고려한 수익 개선 효과 검증을 나타내었다. 이동거리와 시간을 고려한 수익을 그래프로 나타내어 기존 해외 논문에서 검증되었던 One-way 차량 재배치 카셰어링 시스템과 현재 우리나라에서 시행되는 이벤트를 고려한 One-way 차량 재배치 카셰어링 시스템을 비교 분석하였다. 특히, 이동거리와 이용시간에 따른 최대수익을 다중선형회귀분석방법을 통해 평가하였으며, 최대손해액을 고려한 최대추정손실을 나타내었다. 이를 통해 기업이 고객들에게 다양한 마케팅 전략으로 이벤트를 활용한 할인쿠폰을 제시하여 이용고객 수요를 늘리는데 초점을 두었다. 뿐만 아니라 비선형 회귀분석을 통해 실제 이동거리와 시간에 따른 최대수익을 결정하는 상관관계 분석을 보여주고 있다.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1524-1524
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• 2001

A previous paper(Ito et al., 2000) has described the improvement of the standard error(SEC and SEP) of the predicted soluble solids(Brix) in a melon cultivar by non-contact mode with a fiber optic probe. Then we examined the immature and mature fruits. The objective of this study was to determine if non-contact mode could improve the standard error of the predicted Brix of matured melon fruits from cross progeny as well as the contact mode(usual method). The optical absorption spectrum was measured using a NIR Systems model 6500 spectrophotometer. A commercial spectral program(NSAS ver. 3.27) was used for multiple linear regression analysis. Absorbances of 902 and in the vicinity of 877 nm were included as the independent variables in both multiple regression equations. These wavelengths are key wavelengths for non-destructive Brix determination. When the results for the contact mode and non-contact mode are compared, the latter mode improved the former standard error(SEP and RMS).

• Tribology and Lubricants
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• 제26권6호
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• pp.329-335
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• 2010

Recently, the End-milling processing is needed the high-precise technique to get a good surface roughness and rapid time in manufacturing of precision machine parts and electronic parts. The optimum surface roughness has an effect on end-milling working condition such as, cutting direction, spindle speed, feed rate and depth of cut, and so on. It needs to form the correlation of working conditions and surface roughness. Therefore this study was carried out to presume of surface roughness on end-milling working condition of Al7075 by regression analysis. The results was shown that the coefficient of determination($R^2$) of regression equation had a fine reliability of 87.5% and nonlinear regression equation of surface rough was made by multiple regression analysis.

• 분석과학
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• 제12권5호
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• pp.375-381
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• 1999

본 연구는 기존에 사용되고 있는 음주측정기의 부정확성과 비위생적인 면을 개선하기 위한 비침습적인 알코올 측정기를 개발하기 위해 근적외선분광분석법을 적용하였다. 먼저 근적외선분광분석법으로 혈중 알코올을 측정하기 위한 전 단계로 순수한 알코올을 0.01~0.1%의 농도로 함유한 검체를 측정하였다. MLR(multiple linear regression)방법을 통한 통계적 처리에서 1360, 2256, 2012, 그리고 1358 nm의 네 파장을 선택했을 때 SEC(standard error of calibration)은 0.0039, multiple R은 0.99를 나타냈다. 혈중 알코올 시료 측정시 MLR을 적용했을 때 2266과 2326 nm 파장을 선택했을 경우 가장 유의성 있는 결과를 나타냈다. 또 다른 통계적 방법인 PLSR(partial least squares regression)의 경우 이차 미분 스펙트럼의 1100~1340, 1500~1796, 그리고 2064~2300 nm의 범위에서 4개의 factor를 사용했을 때 0.030의 SEP값을 나타냈다. 이로서 근적외선분광분석법을 이용하여 혈액 중의 알코올을 신속하게 분석할 수 있는 가능성을 제시하였다.

• 산업경영시스템학회지
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• 제47권1호
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• pp.41-50
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• 2024

Korea is facing a significant problem with historically low fertility rates, which is becoming a major social issue affecting the economy, labor force, and national security. This study analyzes the factors contributing to the regional gap in fertility rates and derives policy implications. The government and local authorities are implementing a range of policies to address the issue of low fertility. To establish an effective strategy, it is essential to identify the primary factors that contribute to regional disparities. This study identifies these factors and explores policy implications through machine learning and explainable artificial intelligence. The study also examines the influence of media and public opinion on childbirth in Korea by incorporating news and online community sentiment, as well as sentiment fear indices, as independent variables. To establish the relationship between regional fertility rates and factors, the study employs four machine learning models: multiple linear regression, XGBoost, Random Forest, and Support Vector Regression. Support Vector Regression, XGBoost, and Random Forest significantly outperform linear regression, highlighting the importance of machine learning models in explaining non-linear relationships with numerous variables. A factor analysis using SHAP is then conducted. The unemployment rate, Regional Gross Domestic Product per Capita, Women's Participation in Economic Activities, Number of Crimes Committed, Average Age of First Marriage, and Private Education Expenses significantly impact regional fertility rates. However, the degree of impact of the factors affecting fertility may vary by region, suggesting the need for policies tailored to the characteristics of each region, not just an overall ranking of factors.

• 한국컴퓨터정보학회논문지
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• 제22권7호
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• pp.31-37
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• 2017

There are lots of combined battlefield elements which complete the war. It looks problematic when collecting and analyzing these elements and then predicting the situation of war. Commander's experience and military power assessment have widely been used to come up with these problems, then simulated combat training program recently supplements the war-game models through recording real-time simulated combat data. Nevertheless, there are challenges to assess winning factors of combat. In this paper, we characterize the combat element (ce) by clustering simulated combat data, and then suggest multi-layered artificial neural network (ANN) model, which can comprehend non-linear, cross-connected effects among ces to assess mission completion degree (MCD). Through our ANN model, we have the chance of analyzing and predicting winning factors. Experimental results show that our ANN model can explain MCDs through networking ces which overperform multiple linear regression model. Moreover, sensitivity analysis of ces will be the basis of predicting combat situation.