• Communications for Statistical Applications and Methods
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• v.17 no.4
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• pp.561-574
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• 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
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• v.28 no.6
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• pp.1191-1208
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• 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.

• Land and Housing Review
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• v.15 no.1
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• pp.39-55
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• 2024

The aim of this study is to analyse housing satisfaction among residents of different types of public rental housing-permanent, national, and happy housing-following the integration of housing types upon the full-scale supply of integrated public rental housing. By identifying key factors that influence residential satisfaction, our goal is to inform the planning of public rental housing complexes and derive policy implications. The study focuses on analysing discrepancies in residential satisfaction among residents of different types of public rental housing and comparing the factors influencing this satisfaction. Microdata from the Ministry of Land, Infrastructure, and Transport's 'Housing Situation Survey' in 2021 is utilized for analysis, employing one-way ANOVA and binomial logistic regression methods. Empirical analysis reveals variations in residential satisfaction levels between residents of permanent and national rental housing, with national rental housing residents exhibiting higher satisfaction. The influencing factors of overall condition satisfaction are consistent for permanent and national rental residents but differ for happy housing residents. Additionally, the influencing factors of overall residential environmental satisfaction vary across all three housing types. Nonetheless, common factors across all types include housing noise and facility accessibility, highlighting their significance in complex planning. Subsequent studies may involve time series analysis to assess changes in influencing factors over time.

• 한국노년학
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• v.38 no.4
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• pp.1107-1125
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• 2018

The purpose of this study was to present the empirical data for the prevention of suicide by analyzing the group differences according to the types of risk factors and protective factors of family members living with dementia patients and the effects of each type on suicidal ideation. This study investigated the characteristics of suicidal ideation among family members of people living with dementia by using a community health survey. It then investigated the effect of each latent group on the suicidal ideation of family members of people living with dementia. Twenty-four risk and protective factors on suicide ideation were analyzed by using Mplus. The four latent classes were high risk - low protective, high risk - high protective, low risk - high protective and low risk - low protective. Multivariate logistic regression analysis showed that the high risk-low protective factor group had the highest suicidal ideation. Based on these results, practical implications and challenges were presented.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.3
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• pp.9-15
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• 2011

This study analyzed contributing factors affecting leg injury severity in pedestrian-vehicle crashes. A Binary Logistic Regression (BLR) method was used to identify the factors. Independent variables include characteristics for pedestrian, vehicle, road, and environmental conditions. The leg injury severity is classified into two classes, which are dependent variables in this study, such as 'severe' and 'minor' injuries. Pedestrian age, collision speed, and the height of vehicle were identified as significant factors for the leg injury. The probabilistic outcome of predicting leg injury severity can be effectively used in not only deriving pedestrian-related safety policies but also developing advanced vehicular technologies for pedestrian protection.

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

• Journal of Korean Society of Transportation
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• v.30 no.3
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• pp.119-127
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• 2012

For traffic safety, it is imperative for motorists to secure their clear view and to maintain a similar speed with others while driving in a lane. Large-sized vehicles at lower speeds, however, are likely to increase the risk of accident when they share a lane with cars. Although to overcome this complication the Korean Road Traffic Act established rules for the safe use of roads, the reality is that the rules are seldom observed strictly. In this light, this study was designed to analyze the severity of truck-involved accidents, thereby providing justification for the need of truck-designated lanes and thus contributing to measuring road safety more precisely. A binomial logistic regression model was applied to analyze the severity of truck-involved accidents. The analysis showed that several variables affect the severity of truck-involved accidents on freeways; i.e., violation against the rule of truck-designated lanes, weather, difference between daytime and nighttime, and parking on road shoulder. Moreover, the strong enforcement will be needed to make motorists observe the rule, because a Wald statistical test showed that the violation against the rule of truck-designated lanes has the largest influence on the severity.

• Journal of the Korean Regional Science Association
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• v.37 no.3
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• pp.19-36
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• 2021

This study examined the determinants of change in residential size according to the patterns of residential mobility in the Seoul Metropolitan Area. Particularly, this study examined the upward and downward in residential size, which is emerging as an important factor in the era of increasing non-face-to-face environment. For the empirical analysis, I used 「2018 Korea Housing Survey」 and employed binary logistic regression model. The empirical analysis shows the change of residential size is statistically significant depending on the direction of geographic. In addition, there are differences in the determinants of change in residential size. When people move within Seoul, housing factors, accessibility, age of residents, and the number of household members can be the determinants. When people move from Seoul to Gyeonggi or Incheon, housing factors, safety, gender, and the number of household members work as determinants. On the other hand, when moving from Gyeonggi or Incheon to Seoul, whether it is studio or not, housing type, accessibility, the number of household members, and the disability of homeownership are the determinants. When moving within Gyeonggi or Incheon, housing factors, Accessibility to green areas, safety, age of resident, income, and the number of household members, are the determinants.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.172-185
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• 2021

As the era of autonomous driving approaches, it is expected to have a significant impact on our lives. When autonomous driving cars emerge, it is necessary to develop an index that can evaluate autonomous driving cars as it enhance the productive value of the car by reducing the burden on the driver. This study analyzed how the autonomous driving era affects commuting time and commuting time satisfaction among office goers using a car in Gyeonggi-do. First, a nonlinear relationship (V) was derived for the commuting time and commuting time satisfaction. Here, the factors affecting commuting time satisfaction were analyzed through a binomial logistic model, centered on the sample belonging to the nonlinear section (70 minutes or more for commuting time), which is likely to be affected by the autonomous driving era. The analysis results show that the variables affected by the autonomous driving era were health, sleeping hours, working hours, and leisure time. Since the emergence of autonomous driving cars is highly likely to improve the influencing variables, long-distance commuters are likely to feel higher commuting time satisfaction.

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