• Journal of the Korean Data and Information Science Society
• /
• v.13 no.2
• /
• pp.129-138
• /
• 2002

This paper deals with logistic regression models for analysing separation rates from majors. The model building procedure shows how to incoporate the effects of some factors causing from three-way nested sampling scheme and discusses what type of characteristics as independent variables directly affecting the rates should be considered.

• Korea Trade Review
• /
• v.47 no.2
• /
• pp.69-88
• /
• 2022

This paper aims to predict Busan's regional product and employment using the logistic regression models and machine learning models. The following are the main findings of the empirical analysis. First, the OLS regression model shows that the main industries such as electricity and electronics, machine and transport, and finance and insurance affect the Busan's income positively. Second, the binomial logistic regression models show that the Busan's strategic industries such as the future transport machinery, life-care, and smart marine industries contribute on the Busan's income in large order. Third, the multinomial logistic regression models show that the Korea's main industries such as the precise machinery, transport equipment, and machinery influence the Busan's economy positively. And Korea's exports and the depreciation can affect Busan's economy more positively at the higher employment level. Fourth, the voting ensemble model show the higher predictive power than artificial neural network model and support vector machine models. Furthermore, the gradient boosting model and the random forest show the higher predictive power than the voting model in large order.

• Journal of the Korean Data and Information Science Society
• /
• v.21 no.1
• /
• pp.21-32
• /
• 2010

Graphical regression is a paradigm for obtaining regression information using plots without model assumptions. The general goal of this approach is to find lowdimensional sufficient summary plots without loss of important information. Model assessments using residual plots are less likely to be successful in models that are not linear. As an alternative approach, marginal model plots provide a general graphical method for assessing the model. We apply the methods of graphical regression and model assessment using marginal model plots to the logistic regression model.

• Journal of the Korean Data and Information Science Society
• /
• v.24 no.6
• /
• pp.1465-1475
• /
• 2013

The chi-square type test statistic is the most commonly used test in terms of measuring testing goodness-of-fit for multinomial logistic regression model, which has its grouped data (binomial data) and ungrouped (binary) data classified by a covariate pattern. Chi-square type statistic is not a satisfactory gauge, however, because the ungrouped Pearson chi-square statistic does not adhere well to the chi-square statistic and the ungrouped Pearson chi-square statistic is also not a satisfactory form of measurement in itself. Currently, goodness-of-fit in the ordinal setting is often assessed using the Pearson chi-square statistic and deviance tests. These tests involve creating a contingency table in which rows consist of all possible cross-classifications of the model covariates, and columns consist of the levels of the ordinal response. I examined goodness-of-fit tests for a proportional odds logistic regression model-the most commonly used regression model for an ordinal response variable. Using a simulation study, I investigated the distribution and power properties of this test and compared these with those of three other goodness-of-fit tests. The new test had lower power than the existing tests; however, it was able to detect a greater number of the different types of lack of fit considered in this study. I illustrated the ability of the tests to detect lack of fit using a study of aftercare decisions for psychiatrically hospitalized adolescents.

• The Korean Journal of Applied Statistics
• /
• v.35 no.2
• /
• pp.311-325
• /
• 2022

For count responses, the situation of excess zeros often occurs in various research fields. Zero-inflated model is a common choice for modeling such count data. Bayesian inference for the zero-inflated model has long been recognized as a hard problem because the form of conditional posterior distribution is not in closed form. Recently, however, Pillow and Scott (2012) and Polson et al. (2013) proposed a Pólya-Gamma data-augmentation strategy for logistic and negative binomial models, facilitating Bayesian inference for the zero-inflated model. We apply Bayesian zero-inflated negative binomial regression model to longitudinal pharmaceutical data which have been previously analyzed by Min and Agresti (2005). To facilitate posterior sampling for longitudinal zero-inflated model, we use the Pólya-Gamma data-augmentation strategy.

• Korean Journal of Organic Agriculture
• /
• v.21 no.2
• /
• pp.139-155
• /
• 2013

The purpose of this study is to analyze the effects of abolishing the low-pesticide agricultural product certification on environmentally friendly farming. A survey was conducted to quantitatively analyze farming practices and factors that change farming practice. It was found that only 17.0% of low-pesticide fruit farmers said that they will change their farming practice into organic or pesticide-free farming. With regard to the factors of farming practice change, binomial logistic regression model was applied for the analysis. In the analysis, it was found that farmers who grow the low-pesticide agricultural product are more likely to change their farming practice into organic or pesticide-free farming, as their expected price of organic or pesticide-free products is high, their area size is small, price premium of low-pesticide agricultural product is low, the frequency of their training is high. It is necessary to enhance the direct payment system to enlarge organic and nonpesticide acreage, and pest management techniques for fruits should be developed for low-pesticide fruit farmers to change their practice into organic and nonpesticide practice. Dissemination of cultivation manual, introduction of insurance to farmers, improvement of certificate system, and advertising and marketing of environment-friendly agricultural products are useful to develop environment-friendly agriculture.

• Journal of Korean Academy of Nursing
• /
• v.42 no.6
• /
• pp.819-832
• /
• 2012

Purpose: This study was conducted to identify risk factors that influence the probability and severity of elder abuse in community-dwelling older adults. Methods: This study was a cross-sectional descriptive study. Self-report questionnaires were used to collect data from community-dwelling Koreans, 65 and older (N=416). Logistic regression, negative binomial regression and zero-inflated negative binomial regression model for abuse count data were utilized to determine risk factors for elder abuse. Results: The rate of older adults who experienced any one category of abuse was 32.5%. By zero-inflated negative binomial regression analysis, the experience of verbal-psychological abuse was associated with marital status and family support, while the experience of physical abuse was associated with self-esteem, perceived economic stress and family support. Family support was found to be a salient risk factor of probability of abuse in both verbal-psychological and physical abuse. Self-esteem was found to be a salient risk factor of probability and severity of abuse in physical abuse alone. Conclusion: The findings suggest that tailored prevention and intervention considering both types of elder abuse and target populations might be beneficial for preventative efficiency of elder abuse.

• The Korean Journal of Community Living Science
• /
• v.27 no.1
• /
• pp.67-81
• /
• 2016

Responsible tourism has become an established area of the tourism industry. This study aims to identify the factors that influence re-participation in responsible tourism in rural Korea. On-site survey was conducted on 436 tourists by seven responsible tourism agencies in Korea. The motivation for responsible tourists was categorized into seven types: family togetherness, escape and relaxation, personal growth, social interaction, various experiences, learning, and natural experience. The estimation of a binary logistic regression model determined the characteristics of responsible tourists who are most likely to opt for re-participation in responsible tourism. Results indicated that important factors for re-participation in responsible tourism were 'age', 'educational level', 'accompany', 'length of stay', and 'motivation'. The results implied that tourists' internal and external factors are important for re-participation in responsible tourism. It is expected that this study will contribute to the market expansion of responsible tourism.

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

• Journal of Korean Society of Transportation
• /
• v.23 no.1
• /
• pp.9-20
• /
• 2005

Accident type and/or factor identification is important in accident reduction planning. The aim of this paper is to apply the hierarchical approach with binomial distribution and logistic regression analysis to find out types and factors, respectively. Based on 2001 Suwon city black spot data, a binomial distribution modeling approach has been applied to diagnose the black spots, with the help of safety performance modeling approach has been applied to diagnose the black spots, with the help of safety performance function. Then, the logistic regression analysis has been employed to identify the critical factors. Some accident remedies are also reviewed in the light of the model outcomes. The proposed research framework sheds light on a different accident related research and can also be successfully applied to similar studies and sites.