• 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.

• 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.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.455-468
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• 2008

Based on the huge baseball game records, the steal plays an important role to affect the result of games. For the research about success or failure of the steal in baseball games, logistic regression models are developed based on 2007 Korean professional baseball games. The analyses of logistic regression models are compared of those of the discriminant models. It is found that the performance of the logistic regression analysis is more efficient than that of the discriminant analysis. Also, we consider an alternative logistic regression model based on categorical data which are transformed from uneasy obtainable continuous data.

• Proceedings of KOSOMES biannual meeting
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• 2017.11a
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• pp.97-97
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• 2017

• Journal of Korean Academy of Nursing
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• v.51 no.1
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• pp.40-53
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• 2021

Purpose: The purpose of this study was to develop and compare the prediction model for suicide attempts by Korean adolescents using logistic regression and decision tree analysis. Methods: This study utilized secondary data drawn from the 2019 Youth Health Risk Behavior web-based survey. A total of 20 items were selected as the explanatory variables (5 of sociodemographic characteristics, 10 of health-related behaviors, and 5 of psychosocial characteristics). For data analysis, descriptive statistics and logistic regression with complex samples and decision tree analysis were performed using IBM SPSS ver. 25.0 and Stata ver. 16.0. Results: A total of 1,731 participants (3.0%) out of 57,303 responded that they had attempted suicide. The most significant predictors of suicide attempts as determined using the logistic regression model were experience of sadness and hopelessness, substance abuse, and violent victimization. Girls who have experience of sadness and hopelessness, and experience of substance abuse have been identified as the most vulnerable group in suicide attempts in the decision tree model. Conclusion: Experiences of sadness and hopelessness, experiences of substance abuse, and experiences of violent victimization are the common major predictors of suicide attempts in both logistic regression and decision tree models, and the predict rates of both models were similar. We suggest to provide programs considering combination of high-risk predictors for adolescents to prevent suicide attempt.

• Journal of Fisheries and Marine Sciences Education
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• v.25 no.5
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• pp.1214-1223
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• 2013

The purpose of this work is to analyze the factors, criteria and importance that shippers in the international logistics industry take into account in choosing third-party logistics, and thereby to propose the directions of the areas and functions that third-party logistics will need to enhance on the basis of Analytic Hierarchy Process (AHP) methodology. To do that, this work collected and analyzed materials about changing environments of logistic industry and previous studies, and especially conducted a questionnaire survey on the factors to choose third-party logistics with the study subjects of shippers in the international logistics industry. To draw attribute factors, this work repeatedly chose and classified candidate factors through Delphi technique on the basis of the data of previous studies. The analysis results are presented as follows: regarding pairwise comparison between logistic cost and logistic service in relation of conflict, logistic service was recognized to be more important than logistic cost; regarding pairwise comparison between corporate capability and logistic cost, logistic cost was recognized to be more important; and regarding comprehensive evaluation, logistic service factors, including accuracy of order handling, service reliability, freight damage and compensation, a degree of fulfillment of promise, quality of transportation, and problem-solving ability, were found to be in high position.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2008.11a
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• pp.93-96
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• 2008

This research performed strength improvement analysis after evaluating strength characteristics by estimated temperatures to evaluate the real time strength performance of 50 to 80 MPa high performance concrete equipped with heat resistance, and the results are as follows. The lesser W/B and the lesser target slump flow value difference, compression strength was shown to increase, and the more curing temperature becomes, the strength increased accordingly. According to the correlation review result of strength improvement analysis by estimated temperature change performed using logistic analysis model, the compression strength value predicted with logistic curve expression and the compression strength value measured in experiment were shown to have similar correlation, and the strength improvement analysis value by logistic model was shown to be estimated good when W/B is high.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.5
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• pp.393-402
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• 2013

The Mahalanobis-Taguchi System (MTS) is a diagnostic and predictive method for multivariate data. In the MTS, the Mahalanobis space (MS) of reference group is obtained using the standardized variables of normal data. The Mahalanobis space can be used for multi-class classification. Once this MS is established, the useful set of variables is identified to assist in the model analysis or diagnosis using orthogonal arrays and signal-to-noise ratios. And other several techniques have already been used for classification, such as linear discriminant analysis and logistic regression, decision trees, neural networks, etc. The goal of this case study is to compare the ability of the Mahalanobis-Taguchi System and logistic regression using a data set.

• Journal of Korean Academy of Nursing
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• v.43 no.2
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• pp.154-164
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• 2013

Purpose: The purpose of this article is twofold: 1) introducing logistic regression (LR), a multivariable method for modeling the relationship between multiple independent variables and a categorical dependent variable, and 2) examining use and reporting of LR in the nursing literature. Methods: Text books on LR and research articles employing LR as main statistical analysis were reviewed. Twenty-three articles published between 2010 and 2011 in the Journal of Korean Academy of Nursing were analyzed for proper use and reporting of LR models. Results: Logistic regression from basic concepts such as odds, odds ratio, logit transformation and logistic curve, assumption, fitting, reporting and interpreting to cautions were presented. Substantial shortcomings were found in both use of LR and reporting of results. For many studies, sample size was not sufficiently large to call into question the accuracy of the regression model. Additionally, only one study reported validation analysis. Conclusion: Nursing researchers need to pay greater attention to guidelines concerning the use and reporting of LR models.

• Journal of Information Technology Services
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• v.23 no.2
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• pp.1-12
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• 2024

This study aims to find ways to increase the declining donation intention by using machine learning techniques. To this end, in order to predict factors that affect donations between the MZ generation and the senior generation, various machine learning algorithms, including logistic regression analysis, are applied to build a model to determine variables that affect donation intention, and provide statistical verification and evaluation indicators. In this study, differences in donation intention by generation were expected as a variable affecting donation intention, and the senior generation was expected to show a higher donation intention tendency than the younger generation. However, although the research results were not statistically significant, the younger generation showed a higher intention to donate, and these results are interpreted to mean that value consumption and ethical consumption, which are important to today's MZ generation, also influenced donations. However, there were differences between generations in the amount of donations, and higher donation amounts were confirmed among the senior generation (those in their 50s or older) than the younger generation. In addition, the results of the logistic regression analysis showed that previous donation experience had a positive effect on future donation intention, and the more motivation and importance of donation and various social participation activities online and offline, the more active one became in donating.