Approximately 40,000 elevators are installed every year in Korea, and they are used as a convenient means of transportation in daily life. However, the continuous increase in elevators has a social problem of increased safety accidents behind the functional aspect of convenience. There is an emerging need to induce preemptive and active elevator safety management by elevator management entities by strengthening the management of poorly managed elevators. Therefore, this study examines domestic research cases related to the evaluation items of the elevator safety quality rating system conducted in previous studies, and develops a statistical model that can examine the effect of elevator maintenance quality as a result of the safety management of the elevator management entity. We review two types: odds ratio analysis and logistic regression analysis models.

본 연구는 한국인 인구집단에서 폭식행동, 음식중독을 식별하고, 해당 증상들이 비만 및 섭식행 동, 정신건강, 인지적 특성과 어떠한 연관성을 보이는지 규명하고자 하였다. 이를 위하여 정상체중 및 비만 체중에 해당하는 한국인 성인 257명을 대상으로 섭식문제(예: 폭식, 음식중독, 음식갈망), 정신건강(예: 우 울), 인지기능(예: 충동성, 정서조절)에 관한 임상심리검사 척도를 측정하였다. 비만 여부와 성별에 따라 그 룹을 나누었을 때, 비만체중 여성에서 폭식행동이 46.6%, 음식중독이 29.3%로 가장 빈도가 높았다. 성향 점수 매칭 후 데이터로 독립성 검정을 수행한 결과, 폭식행동 및 음식중독이 비만체중 집단에서 정상체중 집단보다 더 많이 나타나는 것을 확인하였다. 또한 폭식행동과 음식중독 유무에 각 심리검사 척도 요인이 미치는 영향력을 파악하고자, 전진선택법을 적용한 로지스틱 회귀모델을 구축하였다. 로지스틱 회귀분석 결과, 폭식행동에는 섭식장애, 음식갈망, 상태불안, 정서조절(인지적 재해석) 및 음식중독이 주로 관여하였 고, 음식중독에는 음식갈망, 폭식행동과 함께 비만과 연령의 교호작용, 교육년수가 유의하게 작용하는 것으 로 나타났다. 본 연구는 한국인 성인을 대상으로 한 체계적 연구로서, 폭식행동과 음식중독이 여성 및 비만 인에서 특히 더 많이 나타남을 확인하였다. 폭식행동과 음식중독에는 일부 섭식문제(예: 음식갈망)가 공통되게 관여하나, 정신건강 및 인지적 위험요인에는 차이가 있었다. 따라서 음식중독과 폭식행동은 서로 구 별되는 개념으로 두고, 각각의 기질적·환경적 위험요인을 깊이 있게 탐구하는 것이 필요하다.

이 연구의 목적은 머신러닝 분석방법을 활용하여 대학생의 소속 학과 만족도에 영향을 미치는 주요 요 인을 분석하여 대학생의 진로지도와 중도탈락 예방 관련 정책 및 제도 수립을 위한 기초 연구 자료를 제 공하기 위함이다. 이를 위해 한국교육고용패널 􎟯(KEEP 􎟯)자료의 4년제 대학 진학생 1,298명을 연구대 상으로 머신러닝 분석방법인 로지스틱 회귀분석과 랜덤포레스트 방법을 통하여 분석을 진행하였다. 주요 분석 결과는 다음과 같다. 첫째, 대학 입학년도에는 대학 생활 관련 변수 이외에도 고등학교 재학 시기 및 고등학교 졸업 후 진로 계획과 관련한 설명변수들이 중요도 상위 10개 항목 중 상당수를 차지하였으며, 입학년도와 졸업년도를 제외한 기간에는 전공 학습과 진로활동에 대한 변수들이, 졸업년도에는 취업준비 및 교육훈련 경험 등이 로지스틱 회귀분석과 랜덤포레스트 분석 결과에서 공통적으로 높은 중요도를 기록하였다. 둘째, 두 분석방 법에 따른 학년별 중요도 상위 10개 변수의 일치도는 63.3%로 나타났다. 셋째, 로지스틱 회귀분석과 달리 랜덤포레스트 분석에서는 설문의 응답자가 다수의 척도를 사용하여 응답한 설명변수들이 중요도 상위 10 개 설명변수에 포함된 경우가 상대적으로 많았다. 이 연구는 교육패널 자료를 단일 분석방법이 아닌 두 가지 머신러닝 방법을 사용하여 공통 요소를 도출하고, 결과의 비교를 시도했다는 점에 의의가 있다.

In addition to physical risks such as electrical, chemical, and mechanic ones in the workplace, psychosocial risks are also raising as an important issue in recent years in connection with human rights and work-life balance policies. The purpose of this study is to confirm the degree of effect of the psychosocial risk management plan at the workplace on workers through logistic regression analysis. Input data for logistic regression analysis is the results of a survey of 4,558 people conducted by the Institute for Occupational Safety and Health were used. There are 9 independent variables, including the change a workplace and confidential counseling, and the dependent variable is whether the worker feels the effect on the psychosocial risk management plan. As a result of this study, changes in work organization, dispute resolution procedures, provision of education program, notification of the impact of psychosocial risks on safety and health, and the persons in charge of solving psychosocial problems are shown effective in reducing worker’s psychosocial risks. This study drives which of the management plans implemented to reduce the psychosocial risk of workers in the workplace are effective, so it can contribute to the development of psychosocial risk management plans in the future.

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.

향후 통일이후 북한지역의 급격한 국토개발로 인한 자연 환경 훼손을 미연에 방지하고 한반도의 균형 있는 국토의 보전과 관리를 위해서는 환경계획 기반의 국토계획이 필수적이다. 이러한 환경계획을 위해서는 다양한 환경공간정보를 이용한 국토의 자연환경 우수지역 평가지도 작성은 반드시 필요하다. 이에 본 연구에서는 로지스틱 회귀분석을 통해 북한지역의 자연환경 우수지역 평가지도 구축방안을 제시하였다. 자연환경 우수성 평가 선행연구를 기반으로 평가에 필요한 평가항목들을 선정하고, 해당 평가항목들을 수집 및 구축하였으며 로지스틱 회귀분석을 이용하여 남북한 접경지역을 중심으로 자연환경성 우수지역 평가를 수행하였다. 평가결과 로지스틱회귀분석 적합성 모형은 89.4%의 분류정확도가 나타났으며, ROC 분석결과 정확도가 96.1%로 높게 나타났다. 본 연구의 결과를 국토환경성평가지도 환경생태적평가와 비교한 결과 본 연구에서 제시한 자연환경 우수지역 평가지도 결과가 북한지역의 환경계획에 활용이 가능할 수 있는 정확도를 나타내고 있다고 판단되었다. 따라서, 본 연구에서 도출한 자연환경 우수지역 평가지도의 40% 이상의 우수지역을 자연환경 핵심 우수지역으로 지정하고, 60%까지의 완충 자연환경성 우수지역으로 지정하여, 추후 단계적으로 핵심 자연환경 우수지역으로 확장할 수 있도록 정책적으로 설정하는 것이 바람직할 것으로 판단된다.

The purpose of this study was to investigate the environmental factors that affect the computer literacy of childcare teachers. A survey was conducted to get information about teachers' ICT utilization ability and their physical and educational condition. Binary logistic regression analysis was performed using SPSS 17.0 program on the data of 293 teachers who work in childcare centers in capital area, and the following results were obtained. First, computer-related physical environment and computer training did not affect the teachers' computer literacy. Second, taking computer course had effects on the basic skills, word-processing ability, internet communication ability and computer program utilization. Third, the number of hours of using computers affected the ability to install and maintain computers. Fourth, the purchase of computer-related books and magazines and the number of computer programs they use had an effect on their computer program utilization. These results have significance in that they imply what is needed for improving childcare teachers' computer literacy in terms of institutional support.

East Nusa Tenggara (ENT) is one of seven provinces in the eastern region of Indonesia that contribute to the large number of out of school children (OOSC). A research study has been carried out to investigate the characteristics of OOSC and to determine the statistical model explaining factors that influence the OOSC occurrence in the age group 13 – 15 years in ENT. Data of OOSC were obtained from the Education Department and Regional Planning Board in 6 selected districts in ENT that were produced from the community based education information system (CBEIS) survey in coordination with UNICEF Kupang in 2013. The districts were Sikka, Timor Tengah Selatan (TTS), West Sumba, East Sumba, Central Sumba and the City of Kupang. A response variable of the study was the state of the children’s education with the category ‘yes’ for school and ‘no’ for out of school. Data was then analysed using descriptive analysis and multiple logistic regression method. The analysis shows that there were 795 OOSC in 10350 children in the junior high school age group. The majority of them are males, living in the country side, have farmer parents, are from families with wealth quintile on the bottom class and have mothers with no education. Logistic analysis on the best model shows that literacy, working status, disability, occupation of household heads, wealth quintile, possession of birth certificate, living status are the factors that significantly affect the number of OOSC in the 13-15 age group. Odds ratio values of the first three factors are 26.5; 12.8 and 7.5 respectively.

In this paper, we have considered the modeling and analyses of categorical data. We modeled binary data with categorical predictors, using logistic regression to develop a statistical method. We found that ANOVA-type analyses often performed unsatisfactory, even when using arcsine-square-root transformations. We concluded that such methods are not appropriate, especially in cases where the fractions were close to 0 or 1. The logistic transformation of fraction data could be a promising alternative, but it is not desirable in the statistical sense. The major purpose of this paper is to demonstrate that logistic regression with an ANOVA-model like parameterization aids our understanding and provides a somewhat different, but sound, statistical background. We examined a simple real-world example to show that we can efficiently test the significance of regression parameters, look for interactions, estimate confidence intervals, and calculate the difference between the mean values of the referent and experimental subgroups. This paper demonstrates that precise confidence interval estimates can be obtained using the proposed ANOVA-model like approach. The method discussed here can be extended to any type of fraction data analysis, particularly for experimental design.

OSHA(Occupational Safety and Health Act) generally regulates employer's business principles in the workplace to maintain safety environment. This act has the fundamental purpose to protect employee's safety and health in the workplace by reducing industrial accidents. Authors tried to investigate the correlation between 'occupational injuries and illnesses' and level of regulation compliance using Survey on Current Status of Occupational Safety & Health data by the various statistical methods, such as generalized regression analysis, logistic regression analysis and poison regression analysis in order to compare the results of those methods. The results have shown that the significant affecting compliance factors were different among those statistical methods. This means that specific interpretation should be considered based on each statistical method. In the future, relevant statistical technique will be developed considering the distribution type of occupational injuries.

An attempt is given to the problem of analyzing the two-way binary attribute data using the logistic regression model in order to find a sound statistical methodology. It is demonstrated that the analysis of variance (ANOVA) may not be good enough, especi

In this paper, an analysis of two-way binary attribute data is performed using the logistic regression model in order to find a sound statistical methodology. It is demonstrated that the ANOVA may not be enough, especially for the case that the proportion is very low or high. The logistic transformation of proportion data could be a help, but not sound in the statistical sense. The adoption of generalized least squares(GLS) method entails much to estimate the variance-covariance matrix. On the other hand, the logistic regression methodology provides sound statistical background in estimating model parameters and related confidence intervals. The efficiencies of estimates are ensured with a simulated data with a view to demonstrate the usefulness of the methodology.

A probability prediction model for tropical cyclone (TC) genesis in the Northwestern Pacific area was developed using the logistic regression method. Total five predictors were used in this model: the lower-level relative vorticity, vertical wind shear, mid-level relative humidity, upper-level equivalent potential temperature, and sea surface temperature (SST). The values for four predictors except for SST were obtained from difference of spatial-averaged value between May and January, and the time average of Niño-3.4 index from February to April was used to see the SST effect. As a result of prediction for the TC genesis frequency from June to December during 1951 to 2007, the model was capable of predicting that 21 (22) years had higher (lower) frequency than the normal year. The analysis of real data indicated that the number of year with the higher (lower) frequency of TC genesis was 28 (29). The overall predictability was about 75%, and the model reliability was also verified statistically through the cross validation analysis method.

본 연구의 목적은 사고위치별(유입부, 유출부, 교차로내 및 횡단보도) 로지스틱 회귀 교통사고 모형을 개발하는 것이다. 충북지방경찰청의 2004~2005년도 사고 자료와 현장조사 자료를 근거로, 교통사고와 관련된 기하구조 요소, 환경 요소 등이 분석되었다. 개발된 모형은 카이제곱 p 값은 0.000 그리고 Nagelkerke R2값 0.363~0.819로 모두 통계적으로 유의한 것으로 분석된다. 개발된 모형의 공통 사고요인은 교통량, 횡단거리 및 좌회전전용차로이며, 특정변수는 교차로내 사고모형의 부도로 교통량, 그리고 횡단보도 사고모형의 주도로 U턴인 것으로 나타나고 있다. Hosmer & Lomeshow 검정은 유입부를 제외한 모형들은 p값이 0.05보다 크기 때문에 통계적으로 적합한 것으로 평가된다. 또한 정분류율 결과는 모든 모형식이 73.9% 이상으로 높은 예측력을 보이는 것으로 분석된다.

We adapted association rules of data mining in order to investigate the relation among the factors of musculoskeletal disorders and proposed the method of preventing the musculoskeletal disorders associated with multiple logistic regression in previous study. This multiple logistic regression was difficult to establish the method of preventing musculoskeletal disorders in case factors can't be managed by worker himself, i.e., age, gender, marital status. In order to solve this problem, we devised association rules of factors of musculoskeletal disorders and proposed the interactive method of preventing the musculoskeletal disorders, by applying association rules with the result of multiple logistic regression in previous study. The result of correlation analysis showed that prevention method of one part also prevents musculoskeletal disorders of other parts of body.

본 연구에서는 레이더 자료를 이용하여 호우의 산지효과 발현특성을 분석하고, 이와 관련된 호우의 특성인자를 선정하여 로지스틱 회귀분석을 수행하였다. 먼저, 충주댐 유역의 산지지역 부근에서 레이더 반사도가 평균반사도에 비해 5% 이상 증가된 호우사상을 산지효과가 발생한 사상으로 판단하였다. 이를 근거로 로지스틱 회귀분석을 수행하기 위해 본 연구에서는 강우강도, 호우의 이동속도, 산지와 호우가 만났을 때 형성된 접근각도를 대상 변량으로 선정하였다. 호우의 특성인자들에 대한 임계값은 강우강도의 경우, 4, 6, 8 mm/hr, 호우의 이동속도의 경우, 4, 6, 8, km/hr, 접근각도의 경우, 산지와 호우가 정면으로 만났을 때를 기준으로(90°) 산지와 호우가 이루는 각이 90±5°, 90±15°, 90±25°일 때를 고려하였다. 결과적으로 각각의 조건들로부터 결정된 로지스틱 회귀분석 결과들로부터 충주댐 유역에 대한 산지효과 발현조건을 확인할 수 있었다.