• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.1
• /
• pp.1-16
• /
• 2022

Recently, e-scooters have been attracting attention as eco-friendly modes of transportation in cities due to an increasing interest in the environment. Accordingly, various studies on usage behavior are being conducted, but studies that reflect individual travel attitudes are insufficient. Therefore, this study surveyed commuters in Seoul and analyzed respondents' traveling attitudes through factor analysis. It also built a binary logistic regression model for the intention to use shared e-scooters to determine how individual travel behaviors are affected. In particular, the model results showed that age, the main mode of transportation (car), walking time to the bus stop, and four travel attitude variables (disutility of travel, preference to self-drive, internet/smartphone friendliness, and willingness to pay extra money for services) significantly affected the intention to use shared e-scooters. This study is expected to be used as basic data, with aspect to travel behavior, for the efficient operation and use of shared e-scooters in the future.

• The Korean Journal of Applied Statistics
• /
• v.22 no.2
• /
• pp.297-307
• /
• 2009

Korean household debt is one of the main sources of the current financial crisis. This paper studies the impact of household members' attributes such as a type of housing(self-own or rent), education, age, average monthly income of the head of household, and the area of residence, on the stress level of the household members due to household debt. We analyze a real data set collected by KB Kookmin Bank in 2004. We consider low and high stress level as a binary response variable and use a logistic regression model with the attributes of household members as explanatory variables. A simple but well-fitting model is selected by backward elimination method based on the likelihood statistic for goodness-of-fit test, and the impact of the attributes on the stress level is studied from parameter estimates of the selected model. We also perform the similar analysis on a binary response variable which distinguishes households with no debt from the rest. From the analysis, the stress level tends to be low for households with self-own houses, high average monthly income, low education level, and young members.

• Journal of the Korean Geotechnical Society
• /
• v.31 no.6
• /
• pp.59-69
• /
• 2015

Recently, landslide and debris-flow disasters caused by severe rain storms have frequently occurred. Many researches related to landslide susceptibility analysis and debris-flow hazard analysis have been conducted, but there are not many researches related to mobilization analysis for landslides transforming into debris-flow in slope areas. In this study, statistical analyses such as discriminant analysis and logistic regression analysis were conducted to develop a mobilization criterion using geomorphological and geological factors. Ten parameters of geomorphological and geological factors were used as independent variables, and 466 cases (228 non-mobilization cases and 238 mobilization cases) were investigated for the statistical analyses. First of all, Fisher's discriminant function was used for the mobilization criterion. It showed 91.6 percent in the accuracy of actual mobilization cases, but homogeneity condition of variance and covariance between non-mobilization and mobilization groups was not satisfied, and independent variables did not follow normal distribution, either. Second, binomial logistic analysis was conducted for the mobilization criterion. The result showed 92.3 percent in the accuracy of actual mobilization cases, and all assumptions for the logistic analysis were satisfied. Therefore, it can be concluded that the mobilization criterion for debris-flow using binomial logistic regression analysis can be effectively applied for the prediction of debris-flow hazard analysis.

• The Journal of the Korea Contents Association
• /
• v.20 no.7
• /
• pp.118-126
• /
• 2020

The purpose of the study is to analyze the relationship between the warning picture on a cigarette pack and non-smoking attempt, which is expected to contribute to the negative perception of smoking as a research subject about smoking adolescents. An online survey data of the Youth Health Behavior in 2018 has been used, and 3,722 adolescents who are currently smokers were selected for the study. For the measurement of variables, demographic sociology, health-related, and smoking-related factors have been revised, and multivariate binomial logistic regression analysis has been performed. The perception rate of cigarette warning pictures among adolescents who smoke currently is 84.7%, and among them, the attempt rate to quit smoking is 72.8%. As a result of the multivariate binomial logistic regression analysis, there is a meaningful relationship between adolescent smokers' attempts to quit smoking and whether they perceived cigarette pack warning pictures, and school grade year, academic performance, stress perception, and ease of purchasing cigarettes have been also expressed as meaningful variables. To be based on the result, it is necessary to manufacture to design a cigarette pack warning picture that can be easily recognized by smoking adolescents in the future.

• The Korean Journal of Applied Statistics
• /
• v.28 no.6
• /
• pp.1191-1208
• /
• 2015

Most statistical methods are dependent on the summary statistic. However, with graphical approaches, it is easier to identify the characteristics of the data and detect information that cannot be obtained by the summary statistic. We present various graphical methods to assess the adequacy of models in logistic regression that include checking log-density ratio, structural dimension, marginal model plot, chi-residual plot, and CERES plot. Through simulation data, we investigate and compare the results of graphical approaches under diverse conditions.

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

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.23 no.2
• /
• pp.15-31
• /
• 2024

The characteristics of elderly traffic accidents were identified by reflecting the situation of the elderly population in Korea, which is entering an ultra-aging society, and the relationship between independent and dependent variables was analyzed by classifying traffic accidents of serious or higher and traffic accidents of minor or lower in elderly pedestrian traffic accidents using binomial variables. Data collection, processing, and variable selection were performed by acquiring data from the elderly pedestrian traffic accident analysis system (TAAS) for the past 10 years (from 13 to 22 years), and basic statistics and analysis by accident factors were performed. A total of 15 influencing variables were derived by applying the logistic regression model, and the influencing variables that have the greatest influence on the probability of a traffic accident involving severe or higher elderly pedestrians were derived. After that, statistical tests were performed to analyze the suitability of the logistic model, and a method for predicting the probability of a traffic accident according to the construction of a prediction model was presented.

• Communications for Statistical Applications and Methods
• /
• v.17 no.4
• /
• pp.561-574
• /
• 2010

We studied the performance of 8 data mining algorithms including decision trees, logistic regression, LDA, QDA, Neral network, and SVM and their combinations of 2 ensemble techniques, bagging and boosting. In this study, we utilized 13 data sets with binary responses. Sensitivity, Specificity and missclassificate error were used as criteria for comparison.

• The Korean Journal of Applied Statistics
• /
• v.30 no.5
• /
• pp.681-690
• /
• 2017

Various imbalanced binary classification problems exist such as fraud detection in banking operations, detecting spam mail and predicting defective products. Several sampling methods such as over sampling, under sampling, SMOTE have been developed to overcome the poor prediction performance of binary classifiers when the proportion of one group is dominant. In order to overcome this problem, several sampling methods such as over-sampling, under-sampling, SMOTE have been developed. In this study, we investigate prediction performance of logistic regression, Lasso, random forest, boosting and support vector machine in combination with the sampling methods for binary imbalanced data. Four real data sets are analyzed to see if there is a substantial improvement in prediction performance. We also emphasize some precautions when the sampling methods are implemented.

• Journal of the Korean Data and Information Science Society
• /
• v.20 no.5
• /
• pp.819-827
• /
• 2009

This study was conducted to investigate the relationship between the sociodemographic factors and the Korean consumers palatability evaluation grades with Hanwoo sensory evaluation data. 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, and beef cut and the the palatability grade as the dependent variable. Stepwise variable selection procedure is incorporated to find the final model and odds ratios are calculated to find the associations between categories.