• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.857-870
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• 2010

This study was conducted to investigate the relationship between the socio-demographic factors and the Korean consumers palatability evaluation grades with Hanwoo sensory evaluation data from 2006 to 2008 by National Institute of Animal Science. The dichotomy logistic regression model and the multinomial logistic regression model are fitted with the independent variables such as the consumer living location, age, gender occupation, monthly income, beef cut and the the palatability grade as the categorical dependent variable and tenderness, 리avor and juiciness as the continuous dependent variable. Stepwise variable selection procedure is incorporated to find the final model and odds ratios are calculated to nd the associations between categories.

• International Journal of Highway Engineering
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• v.13 no.2
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• pp.49-56
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• 2011

Understanding various casual factors affecting the occurrence of freeway traffic crash is a backbone of deriving effective countermeasures. The first step toward understanding such factors is to identify crash risks on freeways. Unlike existing studies, this study focused on the unsafe vehicle maneuvering that can be detected by in-vehicle sensors. The recent advancement of sensor technologies allows us to gather and analyze detailed microscopic events leading to crash occurrence such as the abrupt change in acceleration. This study used an accelerometer to capture the unsafe events. A set of candidate variables representing unsafe events were derived from analyzing acceleration data obtained by the accelerometer. Then, the crash risk was modeled by the binary logistic regression technique. The probabilistic outcome of crash risk can be provided by the proposed model. An application of the methodology assessing crash risk was presented, and further research items for the successful field implementation were also discussed.

• Journal of Korean Society of Transportation
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• v.32 no.3
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• pp.198-206
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• 2014

According to the statistics of the National Police Agency, red light running (RLR) crashes represent a significant safety issue throughout Korea. This study deals with the RLR crashes occurred at signalized intersections in Cheongju. The objectives of this study are to comparatively analyze the characteristics of between RLR crashes and the Non-RLR crashes, and to find out factors using a Binary Logistic Regression(BLR) model. In pursuing the above, the study gives particular attentions to testing the differences between the above two groups with the data of 2,246 RLR/ 3,884 Non-RLR crashes (2007-2011). The main results are as follows. First, many RLR crashes were occurred in the nighttime and in going straight. Second, the difference between RLR and Non-RLR crashes were clearly defined by crash type, maneuver of vehicle before crash, age of driver (30s, 50s), alcohol use and accident pattern. Finally, a statistically significant model (Hosmer and Lemeshow test : 7.052, p-value : 0.531) was developed through the BLR model.

• International Journal of Highway Engineering
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• v.13 no.4
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• pp.199-209
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• 2011

In the study of traffic safety, the analysis on factors affecting crash severity and the understanding about their relationship is important to be planning and execute to improve safety of road and traffic facilities. The purpose of this study is to develop a hierarchical binomial logistic model to identify the significant factors affecting fatal injuries and vehicle damages of traffic crashes on freeway. Two models on death and total vehicle damage are developed. The hierarchical structure of response variable is composed of two level, crash-occupant and crash-vehicle. As a result, we have gotten the crash-level random effect from these hierarchical structure as well as the fixed effect of covariates, namely odds ratio. The crash on the main line and in-out section have greater damage than other facilities. Injuries and vehicle damages are severe in case of traffic violations, centerline invasion and speeding. Also, collision crash and fire occurrence is more severe damaged than other crash types. The surrounding environment of surface conditions by climate and visibility conditions by day and night is a significant factor on crash occurrence. On the orher hand, the geometric condition of road isn't.

• Journal of the Korea Institute of Building Construction
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• v.24 no.1
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• pp.121-131
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• 2024

This research explores the potential of leveraging unstructured data from construction supervision documents, which contain detailed inspection insights from independent third-party monitors of building construction processes. With the evolution of analytical methodologies, such unstructured data has been recognized as a valuable source of information, offering diverse insights. The study introduces a framework designed to assess cost performance by applying advanced analytical methods to the unstructured data found in final construction supervision reports. Specifically, key phrases were identified using text mining and social network analysis techniques, and these phrases were then analyzed through binomial logistic regression to assess cost performance. The study found that predictions of cost performance based on unstructured data from supervision documents achieved an accuracy rate of approximately 73%. The findings of this research are anticipated to serve as a foundational resource for analyzing various forms of unstructured data generated within the construction sector in future projects.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.887-894
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• 2018

An electric vehicle is emerging as an alternative to the response of global climate change and sustainability. However, an Electric vehicle has not been popular due to the constraints such as its price or technical limitations. In order to analyze the effect of purchasing electric vehicles, this study conducted a binary logistic regression model that demonstrates the relation between purchasing and influencing variables. Variables which have high correlation were excluded from the model through the correlation analysis to prevent multicollinearity. Socio-economic variables such as the number of owned vehicles, sex, ages are not significant. On the other hand, Variables related to prices, charging and policy are found to have a significant to effect on the purchase of electric vehicles. In accordance with the model estimated result, it seems to be necessary to improve the charging incentives, or to provide electric car information and to expand opportunities for experience electric vehicles. The result is also expected to be helpful for spreading electric vehicles and formulating policies.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.309-315
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• 2022

This study evaluates potential factors in various levels that affect students' intent to leave school. Those levels include individual, family, and school. After thorough review of literature on related subjects, the data from 「2021 Youth Survey on Human Right Conditions」 will be analyzed. Binary logistic regression analysis shows several results. First of all, respondents' age and sex strongly influence students' intents to leave school. Secondly, in terms of effect size, respondents' age is strongly related to the dependent variable in all models. Third, compared to those variables in individual and family levels, the effects of variables in school level are more significantly related to the intents to leave school. Finally, the significance of the effect of students' independent activities in school on the intent to leave school implies that students' voluntary and independent activities in school could decrease students' frustration in school and increase motivation to stay in school in certain ways.

• Journal of Korean Society of Transportation
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• v.34 no.1
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• pp.29-42
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• 2016

Speeding is an important factor in traffic safety. Speed not only affects crash severity, but is also related to the possibility of crash occurrence. This study presents results from an analysis of 27,968 speed violation cases collected from 36 red light and speed enforcement cameras at signalized intersections in the city of Cheongju. Data included details of their violation history such as speeding tickets within a recent 3-year span and their demographic characteristics. The goal of this analysis is to understand the correlation between speed violations and various factors in terms of humans, vehicles and road environments. This study used descriptive statistics and Binary Logistics Regression(BLR) analysis with SPSS 20.0 software. The major results of this study are as follows. First, speed violations occurred at rural and suburban area. Second, about 25.6% of the violators committed to more than 20km/h over a speed limit. Third, the difference between speed violators and normal drivers clearly appeared in location of intersection(urban/rural/suburban area), gender and age. Finally, a statistically significant model(Hosmer and Lemeshow test: 11.586, p-value: 0.171) was developed through the BLR.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.141-150
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• 2015

Graphical paradigms for assessing the adequacy of models in logistic regression are discussed. The residual plot has been widely used as a graphical tool for evaluating the adequacy of the model. However, this approach works well only for linear models with constant variance, and the alternative approach, the marginal model plot, has its defects as well. We suggest a Chi-residual plot that overcomes the potential shortcomings of the marginal model plot.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.167-174
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• 2019

The purpose of this study was to analyze the factors affecting the drinking behavior of adolescents. Based on this, it aims to suggest the practical and policy measures to prevent the drinking behavior of adolescents and to mediate / reduce them. We used binomial logistic analysis as an analysis method.As a result of this study, the individual factors affecting alcohol drinking were gender, smoking experience over the past year, sexual satisfaction, cyber delinquency, self-esteem, parental abuse, peer as family factors. Peer trust was significantly associated with attachment factors, and school adaptation factors were not found to be associated with alcohol drinking in adolescents. This suggests that multilateral efforts such as individuals, families, and communities are needed to mediate and reduce the drinking behavior of adolescents.