• 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.

• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.145-155
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• 2010

As the green transportation mode, revitalization of bike usage attracts remarkable public attention. For the acquirement of effective outcome, however, the concrete and close analysis about bike utilization characteristics should be arranged first. One result by MLTM(2009) is support this opinion; the bike mode share has been decreased whereas 9,170km of the bicycle path was improved(1995~2007). This study analyzed the bike mode share classified by trip types by using the 303,308 data of Household Travel Survey of Seoul Metropolitan Area, 2006. The highest mode share rate was induced by the institute attendee and Officetel resident as 3.75% and 3.13%, respectively. Also this study established the bike mode share estimation model of Seoul by logistic regression, and analyzed related factors and level of effectiveness related bike demand by calculation of odds ratio in terms of logistic regression coefficients. In conclusion, short trips, institutes district, parks, and Officetel residential area oriented policy should be effective on the revitalization of bike usage.

• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.743-753
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• 2014

Kim et al. (2006) and Kim et al. (2009) reported a set of explanatory variables affecting donation frequency when they analyzed nationwide survey data on donations collected in 2002 by Volunteer 21, a nonprofit organization in Korea. The primary purpose of this paper is to correct computational errors found in Kim et al. (2006) and Kim et al. (2009), to rectify major results in the Tables and Figures and to supplement Kim et al. (2009) by providing new results. We add two logistic regressions to the ZIP and a mixture of two Poisson regressions of Kim et al. (2009). Through these two logistic regressions we could detect a set of explanatory variables affecting donation activity (0 or 1) and another set of explanatory variables, in which the volunteer (0, 1) variable is common, discriminating the infrequent donor group from the frequent donor group.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.4
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• pp.193-202
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• 2008

The objectives of this study are to analyze the spatial site characteristics of existing tombs and the change in the pattern of spatial distributions of tombs over time. The spatial distributions of tombs located in Honam province along the Honam expressway were investigated by interpreting digital aerial photographs taken in two different points of time; 1990 and 2000. According to the results of the study, the tombs newly observed in 2000 photos were located closer to roads and villages than those found in the photos of 1990. This is a finding indicating that the accessibility of tombs has been more important consideration in determining the location of tomb sites. Also found were the gentle slopes of southern aspects to be favored as tomb sites. Based on the data sets of tombs locations and their topographic site characteristics, the probability function of tombs appearance in the study area was derived using the logistic regression analysis technique. As a result, tomb sites were classified as 74.7% by logistic regression. All of six input factors (elevation, slope, aspect, distance from the roads, the town and the stream, respectively) affected the probability of tombs appearance significantly.

• Journal of Digital Convergence
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• v.14 no.12
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• pp.27-33
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• 2016

I would like to analyze the customers accommodation availability of the provisional 'smart board,' having specific mode and style, as a circulation platform of digital contents with using a statistic model in order to find a way and means to activate legal circulation of convergence individual products. The smart board means a circulation platform for both users' convenience and copyright protection, by being conveniently able to upload personal convergence digital contents or apply various licence to the uploaded contents according to the purpose of use. Under these premises of the smart board, this paper is going to focus on verifying to find out which factors, such as users' profile attributes, contents using behaviors, awareness of licence and etc, influence on the intention of using the smart board of general users by using a logistic regression model.

• Land and Housing Review
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• v.14 no.1
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• pp.67-82
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• 2023

Using the Artificial Neural Network model and Binary Logistic Regression model, this study investigates influence factors on the quality of life in terms of housing environment during the COVID-19 in Seoul. The results show that the lower the satisfaction level of housing policy, the lower the quality of life in the employment field and the lower the quality of residential field. On the other hand, permanent workers and self-employed respondents have experienced improvement in residential quality during the pandemic. A limitation of this study is associated with disentangling the causal relationship using the 'black box' characteristics of ANN method.

• Clinics in Shoulder and Elbow
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• v.16 no.1
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• pp.63-72
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• 2013

In medical research, multivariate analysis, especially multiple regression analysis, is used to analyze the influence of multiple variables on the result. Multiple regression analysis should include variables in the model and the problem of multi-collinearity as there are many variables as well as the basic assumption of regression analysis. The multiple regression model is expressed as the coefficient of determination, $R^2$ and the influence of independent variables on result as a regression coefficient, ${\beta}$. Multiple regression analysis can be divided into multiple linear regression analysis, multiple logistic regression analysis, and Cox regression analysis according to the type of dependent variables (continuous variable, categorical variable (binary logit), and state variable, respectively), and the influence of variables on the result is evaluated by regression coefficient${\beta}$, odds ratio, and hazard ratio, respectively. The knowledge of multivariate analysis enables clinicians to analyze the result accurately and to design the further research efficiently.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.31-39
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• 2014

Seasonal influenza epidemics cause 3 to 5 millions severe illness and 250,000 to 500,000 deaths worldwide each year. To prepare better controls on severe influenza epidemics, many studies have been proposed to achieve near real-time surveillance of the spread of influenza. Korea CDC publishes clinical data of influenza epidemics on a weekly basis typically with a 1-2-week reporting lag. To provide faster detection of epidemics, recently approaches using unofficial data such as news reports, social media, and search queries are suggested. Collection of such data is cheap in cost and is realized in near real-time. This research aims to develop regression models for early detecting the outbreak of the seasonal influenza epidemics in Korea with keyword query information provided from the Naver (Korean representative portal site) trend services for PC and mobile device. We selected 20 key words likely to have strong correlations with influenza-like illness (ILI) based on literature review and proposed a logistic regression model and a multiple regression model to predict the outbreak of ILI. With respect of model fitness, the multiple regression model shows better results than logistic regression model. Also we find that a mobile-based regression model is better than PC-based regression model in estimating ILI percentages.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.977-990
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• 2015

Food delivery services are well developed in the Republic of Korea, The increase of one person households and the success of app applications influence delivery services these days. We consider a prediction model for the food delivery service based on weather and dates to predict the number of food delivery services in 2014 using various data mining techniques. We use linear regression, random forest, gradient boosting, support vector machines, neural networks, and logistic regression to find the best prediction model. There are four categories of food delivery services and we consider two methods. For the first method, we estimate the total number of delivery services and the posterior probabilities of each delivery service. For the second method, we use different models for each category and combine them to estimate the total number of delivery services. The neural network and linear regression model perform best in the first method, this is followed by the neural network which is the best for the second method. The result shows that we can estimate the number of deliveries accurately based on dates and weather information.

• Spatial Information Research
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• v.8 no.1
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• pp.85-99
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• 2000

Accurate information on habitat distribution of protected fauna is essential for the habitat management of Korea, a country with very high development pressure. The objectives of this study were to develop a habitat suitability model of wild boar based on GIS and logistic regression, and to create habitat distribution map, and to prepare the basis for habitat management of our country s endangered and protected species. The modeling process of this restudyarch had following three steps. First, GIS database of environmental factors related to use and availability of wild boar habitat were built. Wild boar locations were collected by Radio-Telemetry and GPS. Second, environmental factors affecting the habitat use and availability of wild boars were identified through chi-square test. Third, habitat suitability model based on logistic regression were developed, and the validity of the model was tested. Finally , habitat assessment map was created by utilizing a rule-based approach. The results of the study were as folos. First , distinct difference in wild boar habitat use by season and habitat types were found, however, no difference in wild boar habiat use by season and habitat types were found , however, ho difference by sex and activity types were found. Second, it was found, through habitat availability analysis, that elevation , aspect , forest type, and forest age were significant natural environmental factors affecting wild boar hatibate selection, but the effects of slope, ridge/valley, water, and solar radiation could not be identified, Finally, the habitat at cutoff value of 0.5. The model validation showed that inside validation site had the classification accuracy of 73.07% for total habitat and 80.00% for cover habitat , and outside validation site had the classification accuracy of 75.00% for total habitat.