• Title/Summary/Keyword: Logistic Analysis

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Variable Selection for Logistic Regression Model Using Adjusted Coefficients of Determination (수정 결정계수를 사용한 로지스틱 회귀모형에서의 변수선택법)

  • Hong C. S.;Ham J. H.;Kim H. I.
    • The Korean Journal of Applied Statistics
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    • v.18 no.2
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    • pp.435-443
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    • 2005
  • Coefficients of determination in logistic regression analysis are defined as various statistics, and their values are relatively smaller than those for linear regression model. These coefficients of determination are not generally used to evaluate and diagnose logistic regression model. Liao and McGee (2003) proposed two adjusted coefficients of determination which are robust at the addition of inappropriate predictors and the variation of sample size. In this work, these adjusted coefficients of determination are applied to variable selection method for logistic regression model and compared with results of other methods such as the forward selection, backward elimination, stepwise selection, and AIC statistic.

A Competitiveness Analysis of the Logistic Hub Cities in China (중국 물류거점도시의 경쟁력 분석)

  • Lee, Myung-Hun;Lee, Jun-Yeop
    • Journal of Korea Port Economic Association
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    • v.22 no.4
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    • pp.59-79
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    • 2006
  • In this paper, we analyse the comparative competitiveness of the 10 major logistic hub cities in China. First, using the input distance function, we calculated the technical efficiencies and the opportunity costs of the transport infra structure investments. Then, based on not only these supply side factors but also demand side, the overall comparative competitiveness by cities are analyzed. Our main findings are as follows: early developed, larger cities such as Shanghai, Guangzhou, Shenzhen are technically efficient but their opportunity costs of the additional transport investments are higher than the other cities. We also found that overall competitiveness of these larger and leading logistic hub cities are dominant over the small and newly developed logistic cities.

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Modeling of Breast Cancer Prognostic Factors Using a Parametric Log-Logistic Model in Fars Province, Southern Iran

  • Zare, Najaf;Doostfatemeh, Marzieh;Rezaianzadeh, Abass
    • Asian Pacific Journal of Cancer Prevention
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    • v.13 no.4
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    • pp.1533-1537
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    • 2012
  • In general, breast cancer is the most common malignancy among women in developed as well as some developing countries, often being the second leading cause of cancer mortality after lung cancer. Using a parametric log-logistic model to consider the effects of prognostic factors, the present study focused on the 5-year survival of women with the diagnosis of breast cancer in Southern Iran. A total of 1,148 women who were diagnosed with primary invasive breast cancer from January 2001 to January 2005 were included and divided into three prognosis groups: poor, medium, and good. The survival times as well as the hazard rates of the three different groups were compared. The log-logistic model was employed as the best parametric model which could explain survival times. The hazard rates of the poor and the medium prognosis groups were respectively 13 and 3 times greater than in the good prognosis group. Also, the difference between the overall survival rates of the poor and the medium prognosis groups was highly significant in comparison to the good prognosis group. Use of the parametric log-logistic model - also a proportional odds model - allowed assessment of the natural process of the disease based on hazard and identification of trends.

Making a Hazard Map of Road Slope Using a GIS and Logistic Regression Model (GIS와 Logistic 회귀모형을 이용한 접도사면 재해위험도 작성)

  • Kang, In-Joon;Kang, Ho-Yun;Jang, Yong-Gu;Kwak, Young-Joo
    • Journal of Korean Society for Geospatial Information Science
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    • v.14 no.1 s.35
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    • pp.85-91
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    • 2006
  • Recently, slope failures are happen to natural disastrous when they occur in mountainous areas adjoining highways in Korea. The accidents associated with slope failures have increased due to rapid urbanization of mountainous areas. Therefore, Regular maintenance is essential for all slope and needs maintenance of road safety as well as road function. In this study, we take priority of making a database of risk factor of the failure of a slope before assesment and analysis. The purpose of this paper is to recommend a standard of Slope Management Information Sheet(SMIS) like as Hazard Map. The next research, we suggest to pre-estimated model of a road slope using Logistic Regression Model.

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Landslide susceptibility mapping using Logistic Regression and Fuzzy Set model at the Boeun Area, Korea (로지스틱 회귀분석과 퍼지 기법을 이용한 산사태 취약성 지도작성: 보은군을 대상으로)

  • Al-Mamun, Al-Mamun;JANG, Dong-Ho
    • Journal of The Geomorphological Association of Korea
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    • v.23 no.2
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    • pp.109-125
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    • 2016
  • This study aims to identify the landslide susceptible zones of Boeun area and provide reliable landslide susceptibility maps by applying different modeling methods. Aerial photographs and field survey on the Boeun area identified landslide inventory map that consists of 388 landslide locations. A total ofseven landslide causative factors (elevation, slope angle, slope aspect, geology, soil, forest and land-use) were extracted from the database and then converted into raster. Landslide causative factors were provided to investigate about the spatial relationship between each factor and landslide occurrence by using fuzzy set and logistic regression model. Fuzzy membership value and logistic regression coefficient were employed to determine each factor's rating for landslide susceptibility mapping. Then, the landslide susceptibility maps were compared and validated by cross validation technique. In the cross validation process, 50% of observed landslides were selected randomly by Excel and two success rate curves (SRC) were generated for each landslide susceptibility map. The result demonstrates the 84.34% and 83.29% accuracy ratio for logistic regression model and fuzzy set model respectively. It means that both models were very reliable and reasonable methods for landslide susceptibility analysis.

Logistic Capability and Total Quality Management Practice on SME's Performance

  • MARJAN, Yakuttinah;HASANAH, Uswatun;MULIATIE, Yurilla Endah;USMAN, Indrianawati
    • Journal of Distribution Science
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    • v.20 no.7
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    • pp.97-105
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    • 2022
  • Purpose: This study aims to analyze and prove the effect of logistic capability and Total Quality Management practices on Micro, Small and Medium Enterprises (SME) performance directly or mediated by non-financial performance. Research design, data and methodology: This study tested the hypothesis using Hierarchical multiple regression analysis, the method of data collection in this study was using questionnaire, the sampling technique was purposive sampling technique, with SME that has been established for more than 5 years and manufacturing. The data analyzed were 180 respondents using SPSS 25. Results: The findings showed that logistic capability has direct and indirect effects on SME financial performance and has a positive effect on SME financial performance mediated by non-financial performance. While the total quality management practices have a positive effect on SME financial performance mediated by non-financial performance. Thus, companies can achieve maximum financial performance if they invest in developing employee knowledge and concerning on non-financial actions, such as employee satisfaction, innovation and proactively seeking market opportunities. Conclusions: In conclusion, one of the main factors that companies need to consider to improve financial performance is non-financial performance in mediating the effect of logistic capability and TQM practices on the financial performance of SMEs.

Prediction on Busan's Gross Product and Employment of Major Industry with Logistic Regression and Machine Learning Model (로지스틱 회귀모형과 머신러닝 모형을 활용한 주요산업의 부산 지역총생산 및 고용 효과 예측)

  • Chae-Deug Yi
    • 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.

Comparative Analysis of Determination of Method Location between Classes (클래스 간 메소드 위치 결정 방법의 비교)

  • Jung, Young-Ae;Park, Young-B.
    • The Journal of the Korea Contents Association
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    • v.6 no.12
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    • pp.80-88
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    • 2006
  • In Object-Oriented Paradigm, various cohesion measurements have been studied taking into account reference relation among components - like attributes and methods - that belong to a class. In addition, a number of methods have taken into research utilizing manual analysis, that is performed by developer's intuition and experience, and automatic analysis in refactoring field. The verification of objective criteria is demanded in order to process automatic refactoring. In this paper, we propose a method exploiting logistic regression and neural network for analysis of the relationship between six factors considering reference relation and method location among classes. Experimental results demonstrate that the logistic regression predicts the results up to 97% and the neural network predicts the outcomes up to 90%. Hence, we conclude that the logistic regression based method is more effective to predict the method location. Moreover, more than 90% of experimental results from both methods show that the six factors used in Move Method in refactoring are suitable to be used as an objective criteria.

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The Effectiveness Validation of Psychosocial Risk Management Plans in an Organizational Working Environment Using Logistic Regression Analysis (로지스틱 회귀분석을 이용한 조직 근로환경에서의 심리사회적 위험관리 방안의 효과 검증)

  • Kim, Soo-Yun;Han, Seung-Jo;Lee, Dong-Hyung
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.44 no.2
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    • pp.78-84
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    • 2021
  • In addition to physical risks such as electrical, chemical, and mechanic ones in the workplace, psychosocial risks are also raising as an important issue in recent years in connection with human rights and work-life balance policies. The purpose of this study is to confirm the degree of effect of the psychosocial risk management plan at the workplace on workers through logistic regression analysis. Input data for logistic regression analysis is the results of a survey of 4,558 people conducted by the Institute for Occupational Safety and Health were used. There are 9 independent variables, including the change a workplace and confidential counseling, and the dependent variable is whether the worker feels the effect on the psychosocial risk management plan. As a result of this study, changes in work organization, dispute resolution procedures, provision of education program, notification of the impact of psychosocial risks on safety and health, and the persons in charge of solving psychosocial problems are shown effective in reducing worker's psychosocial risks. This study drives which of the management plans implemented to reduce the psychosocial risk of workers in the workplace are effective, so it can contribute to the development of psychosocial risk management plans in the future.

A Study on Technological Forecasting of Next-Generation Display Technology (차세대 디스플레이 기술의 예측에 관한 연구)

  • Nam, Ki-Woong;Park, Sang-Sung;Shin, Young-Geun;Jung, Won-Gyo;Jang, Dong-Sik
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.10 no.10
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    • pp.2923-2934
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    • 2009
  • This paper presents study on technological forecasting of Next-Generation Display technology. Next-Generation Display technology is one of the emerging technologies lately. So databases on patent documents of this technology were analyzed first. And patent analysis was performed for finding out present technology trend. And the forecast for this technology was made by growth curves which were obtained from forecast models using patent documents. In previous study, Gompertz, Logistic, Bass were used for forecasting diffusion of demand in market. Gompertz, Logistic models which were often used for technological forecasting, too. So, two models were applied in this study. But Gompertz, Logistic models only consider internal effect of diffusion. And it is difficult to estimate maximum value of growth in two models. So, Bass model which considers both internal effect and external effect of diffusion was also applied. And maximum value of growth in Gompertz, Logistic models was estimated by Bass model.