• The Korean Journal of Food And Nutrition
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• v.36 no.4
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• pp.222-235
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• 2023

In this research, we examined the shifts in determinants influencing the frequency of eco-friendly food purchases pre- and post-COVID-19. Our analysis utilized filtered 2019-2021 Consumption Behavior Survey data from the Korea Rural Economic Institute Food, excluding any irrational responses. Given the nature of the dependent variable, a multinomial logistic regression model was employed with demographic factors, variables pertaining to food consumption behavior, and variables concerning food consumption awareness as predictors. Following the onset of the COVID-19 pandemic, an individual's level of education was observed to positively influence the frequency of eco-friendly food purchases. In contrast, income level and fluctuations in food consumption expenditure did not appear to have a discernible impact on the purchasing frequency of such eco-friendly products. Irrespective of the advent of COVID-19, variables such as the frequency of online food purchases, the utilization of early morning delivery services, dining out frequency, and the intake of health-functional foods consistently demonstrated a positive correlation with the propensity to purchase eco-friendly foods. Overall, consumers prioritizing safety, quality, and nutrition over price, taste, and convenience in their procurement decisions for rice, vegetables, meat, and processed foods exhibit an increased inclination toward the acquisition of eco-friendly food products.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.265-285
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• 2018

In this study, we applied a deep neural network model to predict four grades of fine dust $PM_{10}$, 'Good, Moderate, Bad, Very Bad' and two grades, 'Good or Moderate and Bad or Very Bad'. The deep neural network model and existing classification techniques (such as neural network model, multinomial logistic regression model, support vector machine, and random forest) were applied to fine dust daily data observed from 2010 to 2015 in six major metropolitan areas of Korea. Data analysis shows that the deep neural network model outperforms others in the sense of accuracy.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.84-94
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• 2018

In recent years, the diminishing of operation and maintenance cost using advanced maintenance technology is attracting many companies' attention. Especially, the heavy machinery industry regards it as a crucial problem since a failure of heavy machinery requires high cost and long downtime. To improve the current maintenance process, the heavy machinery industry tries to develop a methodology to predict failure in advance and to find its causes using usage data. A better analysis of failure causes requires more data so that various kinds of sensor are attached to machines and abundant amount of product usage data is collected through the sensor network. However, the systemic analysis of the collected product usage data is still in its infant stage. Many previous works have focused on failure occurrence as statistical data for reliability analysis. There have been less works to apply product usage data into root cause analysis of product failure. The product usage data collected while failures occur should be considered failure cause analysis. To do this, this study proposes a methodology to apply product usage data into failure cause analysis. The proposed methodology in this study is composed of several steps to transform product usage into failure causes. Various statistical analysis combined with product usage data such as multinomial logistic regression, T-test, and so on are used for the root cause analysis. The proposed methodology is applied to field data coming from operated locomotive and the analysis result shows its effectiveness.

• Journal of the Korea Convergence Society
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• v.10 no.2
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• pp.75-81
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• 2019

The purpose of this study was to perform a convergence study for the investigation of the trend of research topics related to thyroid cancer in Korea. We collected related research papers from DBpia and employed LDA-based topic model. In result, we identified four research topics, each of which concerns "Surgery", "Disease aggressiveness", "Survival analysis", and "Well-being of patients". With multinomial logistic regression, we found significant time trend, where "Surgery"-related topic was popular before 2000, topics regarding "Disease aggressiveness" and "Survival analysis" were frequently addressed in the 2000s, and "Survival analysis" and especially "Well-being of patients" have been pursued since 2010. The findings would serve as a reference guide for research directions. Future work may examine whether the recent change in research topics is observed in other diseases.

• Archives of Craniofacial Surgery
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• v.13 no.1
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• pp.36-40
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• 2012

Purpose: Statistical analysis of facial bone fractures has been performed in various papers. However, reports on risk factors for facial bone fractures are rare. In order to prevent facial bone fractures, it is important to determine the risk factors for their occurrence. This study seeks to perform a statistical analysis on and identify the risk factors associated with facial bone fractures. Methods: A retrospective study was performed to assess facial bone fractures in patients presenting from October 2009 to January 2011 through a chart review. The data collected included age, gender, etiology, and alcohol consumption. Data was analyzed using multinomial logistic regression analysis. The significance level was set at p<0.05 and SAS ver. 9.2 was used. Results: A total of 489 patients were analyzed. The patients' age ranged from 2 to 85 years (mean age, $31.8{\pm}15.4$ years). The ratio of men to women was 5.0:1. The predominant group was age below 19 years old (30.9%). The main causes of facial bone fractures were assaults (37.8%), falls (27.2%), and sport accidents (19.5%). On multinomial logistic regression analysis, age, especially in the teen group was associated with assaults (p<0.05) resulting in facial bone fractures. Alcohol consumption was significantly associated with assaults and falls (p<0.05) leading to facial bone fractures. Conclusion: Facial bone fracture is a challenging problem, because of its high incidence and financial cost. The findings of this study indicate that more effective policies aimed at reducing alcohol intake and teenage violence are needed.

• Journal of Korean Society of Rural Planning
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• v.26 no.3
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• pp.1-12
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• 2020

The purpose of this study is to analysis the influence of fishing villages experience programs choice by the tourist characteristics. As an analysis method, a statistical technique of multinomial logistic regression was used. The dependent variable have typified about 70 fishing experience programs, such as tidal-flat experience, fishery experience, and fishing experience, operated by the fishing village experience recreation villages into 9 programs. The independent variables consisted of 7 groups of people: gender, age, marital status, presence of children, experience of visiting a village in a rural and fishing village experience, preference of a village in a recreational experience, and recognition of a village in a fishing village experience. As a result of analysis, no significant differences were found that the selection group preferring 'fishing culture experience', 'leports experience', 'ecological craft experience', and 'festival and event experience' in the selection of fishing village experience program compared to the group choosing 'rural experience'. On the other hand, the group preferring 'tidal flat experience' analysis that 'married' is about 14 times higher than 'unmarried', and the group preferring 'fishing village experience' is 9.55 times higher than the group preferring 'rural village experience'. In the group preferring 'fishery experience' and 'fishing experience', the group preferring 'fishing experience recreation village' was 9.21 times and 14.34 times higher than the group preferring 'rural experience recreation village'. In the 'food experience', 'married' was 25 times higher than 'unmarried'.

• Journal of the Korean Society of School Health
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• v.35 no.3
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• pp.133-142
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• 2022

Purpose: The purpose of the study was to investigate the effect of the environment on adolescent binge drinking. Methods: The study was designed as a cross-sectional study. Using statistics from the 17th (20201) Korea Youth Risk Behavior Web-based Survey, the raw data target population was 2,629,588 people, and the sample group used for analysis as the final data was 54,848 people. A Rao-scott 𝑥² test and univariate multinomial logistic regression analysis were performed using IBM SPSS 27.0. Results: In the results of univariate logistic regression analysis and multivariate logistic regression analysis, common related variables were gender, school level, academic achievement, sleep satisfaction, current smoking, daily smoking, and alcohol education experience. Conclusion: As a result of confirming the factors influencing binge drinking in Korean adolescents, some variables that increase the possibility of problematic drinking behavior in the socio-environmental areas such as individuals, communities, and national policies were identified. For effective prevention and intervention, it is necessary to develop programs to build a healthy environmental support system with support from national policies, including individuals, peer groups, and communities.

• Research in Community and Public Health Nursing
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• v.30 no.4
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• pp.449-459
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• 2019

Purpose: The aim of this study is to explore different types of self-rated health trajectories among one-person households in Korea. Methods: We used five time-point data derived from Korea Health Panel (2011~2015). A latent growth curve modeling was used to assess the overall feature of self-rated health trajectory in one-person households, and a latent class growth modeling was used to determine the number and shape of trajectories. We then applied multinomial logistic regression on each class to explore the predicting variables. Results: We found that the overall slope of self-rated health in one-person households decreases. In addition, latent class analysis demonstrated three classes: 1) High-Decreasing class (i.e., high intercept, significantly decreasing slope), 2) Moderate-Decreasing class (i.e., average intercept, significantly decreasing slope), and 3) Low-Stable class (i.e., low intercept, flat and nonsignificant slope). The multinomial logistic regression analysis showed that the predictors of each class were different. Especially, one-person households with poor health condition early were at greater risk of being Low-Stable class compared with High-Decreasing class group. Conclusion: The findings of this study demonstrate that more attentions to one-person households are needed to promote their health status. Policymakers may develop different health and welfare programs depending on different characteristics of one-person household trajectory groups in Korea.

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

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.577-587
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• 2023

Conventional categorical data imputation techniques, such as mode imputation, often encounter issues related to overestimation. If the variable has too many categories, multinomial logistic regression imputation method may be impossible due to computational limitations. To rectify these limitations, we propose a two-stage imputation method. During the first stage, we utilize the Boruta variable selection method on the complete dataset to identify significant variables for the target categorical variable. Then, in the second stage, we use the important variables for the target categorical variable for logistic regression to impute missing data in binary variables, polytomous regression to impute missing data in categorical variables, and predictive mean matching to impute missing data in quantitative variables. Through analysis of both asymmetric and non-normal simulated and real data, we demonstrate that the two-stage imputation method outperforms imputation methods lacking variable selection, as evidenced by accuracy measures. During the analysis of real survey data, we also demonstrate that our suggested two-stage imputation method surpasses the current imputation approach in terms of accuracy.