• Title/Summary/Keyword: Logistic Regression Analysis

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On Logistic Regression Analysis Using Propensity Score Matching (성향점수매칭 방법을 사용한 로지스틱 회귀분석에 관한 연구)

  • Kim, So Youn;Baek, Jong Il
    • Journal of Applied Reliability
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    • v.16 no.4
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    • pp.323-330
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    • 2016
  • Purpose: Recently, propensity score matching method is used in a large number of research paper, nonetheless, there is no research using fitness test of before and after propensity score matching. Therefore, comparing fitness of before and after propensity score matching by logistic regression analysis using data from 'online survey of adolescent health' is the main significance of this research. Method: Data that has similar propensity in two groups is extracted by using propensity score matching then implement logistic regression analysis on before and after matching separately. Results: To test fitness of logistic regression analysis model, we use Model summary, -2Log Likelihood and Hosmer-Lomeshow methods. As a result, it is confirmed that the data after matching is more suitable for logistic regression analysis than data before matching. Conclusion: Therefore, better result which has appropriate fitness will be shown by using propensity score matching shows better result which has better fitness.

Comparison between Logistic Regression and Artificial Neural Networks as MMPI Discriminator (MMPI 분석도구로서 인공신경망 분석과 로지스틱 회귀분석의 비교)

  • Lee, Jaewon;Jeong, Bum Seok;Kim, Mi Sug;Choi, Jee Wook;Ahn, Byung Un
    • 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.

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A Proposal of the Evaluation Method for Rock Slope Stability Using Logistic Regression Analysis (로지스틱 회귀분석을 통한 암반사면의 안정성 평가법 제안)

  • 이용희;김종열
    • Tunnel and Underground Space
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    • v.14 no.2
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    • pp.133-141
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    • 2004
  • Through the many site investigations, different methods for evaluating stability of rock slopes have been proposed. Those methods, however, may lead to different results depending on the subjective judgments associated with the selection of the evaluation items and the application of weighting factor. Accordingly, binary logistic regression analysis was carried out to ensure fair appliction of the weighting factor, leading to an equation for evaluating the stability of rock slopes.

Categorical Analysis for the Factors of Incustrial Accident Cases (산업재해 사례인자의 범주형 분석)

  • Jhee, Kyung-Tek;Song, Young-Ho;Chung, Kook-Sam
    • Journal of the Korean Society of Safety
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    • v.17 no.1
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    • pp.94-98
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    • 2002
  • This study aimed to search for the fundamental accident causes using a categorical analysis, a kind of statistical methods. As the analysis methods, correlation analysis, independence test and logistic regression analysis were used. And the SPSS package, a general-purpose mathematical library, was used to obtain statistical characteristics. As the result of this study, the accident causes associated with factor of 'lost working days' were factors such as 'employed periods', 'sex', 'type of accident', 'month'. In case of applying independence test method, the most important cause was the factor of 'month'. In case that logistic regression analysis method was applied, the cause contributed to the increase structure'. 'less than 6 month'. On the basis of these results, the plan for accident prevention and the proper investment for accident prevention expenditure could be carried out in each workshop.

The Effectiveness Validation of Psychosocial Risk Management Plans in an Organizational Working Environment Using Logistic Regression Analysis (로지스틱 회귀분석을 이용한 조직 근로환경에서의 심리사회적 위험관리 방안의 효과 검증)

  • Kim, Soo-Yun;Han, Seung-Jo;Lee, Dong-Hyung
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.44 no.2
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    • pp.78-84
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    • 2021
  • 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.

FACTORS AFFECTING PATIENTS' DECISION-MAKING FOR DENTAL PROSTHETIC TREATMENT

  • Jung, Hyo-Kyung;Kim, Han-Gon
    • The Journal of Korean Academy of Prosthodontics
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    • v.46 no.6
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    • pp.610-619
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    • 2008
  • STATEMENT OF PROBLEM: Factors affecting patients' decision-making for dental prosthetic treatment should be examined in terms of understanding improving patients' oral health. PURPOSE: The main purpose of this dissertation was to investigate patients' dental prosthetic treatment and factors affecting patients' decision-making for dental prosthesis treatment in Deagu and Gyungbook areas. MATERIAL AND METHODS: This study was based on the preliminary survey of dental patients conducted from July 1 to August 31 in 2006. A total of 700 questionnaires had been distributed and 640 were collected. 629 questionnaires were used for the statistical analysis. Descriptive and inferential statistics, such as frequencies, cross tabulation analysis, correlation analysis, logistic regression analysis, and multiple regression analysis were introduced. In the multiple regression analysis and logistic regression analysis, twenty-two independent variables were employed to explore the factors which have impacts on decision-making and satisfaction. RESULTS: The results of this dissertation are as follows: Logistic regression analysis turned out that monthly income, age, degree of expectation, marital status, and employer-insured policy of national insurance statistically increased the odds of decision-making of dental prosthesis treatment. But educational attainment decreased the odds ratio of the decision-making of dental prosthesis treatment. However, the rest independent variables do not have statistically significant impacts on the decision-making of dental prosthesis treatment CONCLUSION: Among independent variables, marital status had the most significant influence on the decision making of dental prosthesis treatment. Finally, suggestions for the future study and policy implications to improve satisfaction of the patients' dental prosthetic treatment were discussed.

Prediction of Galloping Accidents in Power Transmission Line Using Logistic Regression Analysis

  • Lee, Junghoon;Jung, Ho-Yeon;Koo, J.R.;Yoon, Yoonjin;Jung, Hyung-Jo
    • Journal of Electrical Engineering and Technology
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    • v.12 no.2
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    • pp.969-980
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    • 2017
  • Galloping is one of the most serious vibration problems in transmission lines. Power lines can be extensively damaged owing to aerodynamic instabilities caused by ice accretion. In this study, the accident probability induced by galloping phenomenon was analyzed using logistic regression analysis. As former studies have generally concluded, main factors considered were local weather factors and physical factors of power delivery systems. Since the number of transmission towers outnumbers the number of weather observatories, interpolation of weather factors, Kriging to be more specific, has been conducted in prior to forming galloping accident estimation model. Physical factors have been provided by Korea Electric Power Corporation, however because of the large number of explanatory variables, variable selection has been conducted, leaving total 11 variables. Before forming estimation model, with 84 provided galloping cases, 840 non-galloped cases were chosen out of 13 billion cases. Prediction model for accidents by galloping has been formed with logistic regression model and validated with 4-fold validation method, corresponding AUC value of ROC curve has been used to assess the discrimination level of estimation models. As the result, logistic regression analysis effectively discriminated the power lines that experienced galloping accidents from those that did not.

A Comparative Experiment of Software Defect Prediction Models using Object Oriented Metrics (객체지향 메트릭을 이용한 결함 예측 모형의 실험적 비교)

  • Kim, Yun-Kyu;Kim, Tae-Yeon;Chae, Heung-Seok
    • Journal of KIISE:Computing Practices and Letters
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    • v.15 no.8
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    • pp.596-600
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    • 2009
  • To support an efficient management of software verification and validation activities, many defect prediction models have been proposed based on object oriented metrics. They usually adopt logistic regression analysis, And, they state that the correctness of prediction is about 60${\sim}$70%, We performed a similar experiment with Eclipse 3.3 to check their prediction effectiveness, However, the result shows that correctness is about 40% which is much lower than the original results. We also found that univariate logistic regression analysis produces better results than multivariate logistic regression analysis.

Evaluation of the Relationship between the Results of Blood Test and Sasang Constitution (사상체질과 혈액검사 결과의 연관성 평가)

  • Jeong, Mi Kyung;Youn, Sang Jun;Jun, Chan Yong;Park, Jong Hyeong;Choi, You Kyung
    • Journal of Physiology & Pathology in Korean Medicine
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    • v.26 no.6
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    • pp.964-969
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    • 2012
  • This study aimed to evaluate the relationship between the results of blood test and Sasang constitution. We performed blood test of 2,387 university students in Health examination. Sasang constitution was diagnosed by using the Questionnaire of Sasang Constitution Class II(QSCC II). All the data were analysed statistically by descriptive statistics, ANOVA and logistic regression analysis. The Taeeumin group showed significantly higher AST, ALT, GGT, LDH, CPK, creatinine, uric acid, total cholesterol, TG, and LDL levels than other groups, while having a lower HDL level. According to logistic regression analysis, Hb, RBC, uric acid, creatinine were effective common factors for classifying each constitution groups. In the results of this study, there were significant differences in the results of blood test between the three constitutions. But the blood test was insufficient as an objective indicator for discriminating Sasang constitutions.

A Probabilistic Model for Landslide Prediction (산사태 발생예측을 위한 확률모델)

  • Chae, Byung-Gon;Kim, Won-Young;Cho, Yong-Chan;Song, Young-Suk
    • Proceedings of the Korean Geotechical Society Conference
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    • 2005.03a
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    • pp.185-190
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    • 2005
  • In this study, a probabilistic prediction model for debris flow occurrence was developed using a logistic regression analysis. The model can be applicable to metamorphic rocks and granite area. In order to develop the prediction model, detailed field survey and laboratory soil tests were conducted both in the northern and the southern Gyeonggi province and in Sangju, Gyeongbuk province, Korea. The six landslide triggering factors were selected by a logistic regression analysis as well as several basic statistical analyses. The six factors consist of two topographic factors and four geological and geotechnical factors. The model assigns a weight value to each selected factor. The verification results reveal that the model has 86.5% of prediction accuracy. Therefore, it is possible to predict landslide occurrence in a probabilistic and quantitative manner.

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