• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.857-870
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• 2010

This study was conducted to investigate the relationship between the socio-demographic factors and the Korean consumers palatability evaluation grades with Hanwoo sensory evaluation data from 2006 to 2008 by National Institute of Animal Science. 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, beef cut and the the palatability grade as the categorical dependent variable and tenderness, 리avor and juiciness as the continuous dependent variable. Stepwise variable selection procedure is incorporated to find the final model and odds ratios are calculated to nd the associations between categories.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.571-580
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• 2008

The logistic regression analysis is widely used in the area of customer relationship management and credit risk management. It is well known that the maximum likelihood estimation is not appropriate when multicollinearity exists among the regressors. Thus we propose the logistic principal components regression to deal with the multicollinearity problem. In particular, new method is suggested to select proper principal components. The selection method is based on the condition index instead of the eigenvalue. When a condition index is larger than the upper limit of cutoff value, principal component corresponding to the index is removed from the estimation. And hypothesis test is sequentially employed to eliminate the principal component when a condition index is between the upper limit and the lower limit. The limits are obtained by a linear model which is constructed on the basis of the conjoint analysis. The proposed method is evaluated by means of the variance of the estimates and the correct classification rate. The results indicate that the proposed method is superior to the existing method in terms of efficiency and goodness of fit.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.589-602
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• 2008

This article studies on disability pensioners' characteristic with multinomial logit and logistic regression model. Seven factors are examined on whether each factor is reflected in degree of disability in the disability pension. By incorporating multinomial logit and logistic regression model, effectiveness and characteristic of the seven factors are investigated on the degree of disability. Result shows all the seven factors are significant on the degree of disability, while among the seven, five factors, age, sex, type of coverage, type of category, insured duration show a trend in degree of disability and the other two, cause of disability and class of standard monthly income are not effective on trend in degree of disability. Results from analyses might be useful for disability pension management.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.531-539
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• 2009

Logistic regression is widely used as a datamining technique for the customer relationship management. The maximum likelihood estimator has highly inflated variance when multicollinearity exists among the regressors, and it is not robust against outliers. Thus we propose the robust principal components logistic regression to deal with both multicollinearity and outlier problem. A procedure is suggested for the selection of principal components, which is based on the condition index. When a condition index is larger than the cutoff value obtained from the model constructed on the basis of the conjoint analysis, the corresponding principal component is removed from the logistic model. In addition, we employ an algorithm for the robust estimation, which strives to dampen the effect of outliers by applying the appropriate weights and factors to the leverage points and vertical outliers identified by the V-mask type criterion. The Monte Carlo simulation results indicate that the proposed procedure yields higher rate of correct classification than the existing method.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.691-699
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• 2011

Land sliding is one of well-known nature disaster. As a part of effort to reduce damage from land sliding, many researchers worked on increasing prediction ability. However, because previous studies are conducted mostly by non-statisticians, previously proposed models were hardly statistically justifiable. In this paper, we predicted the probability of land sliding using the logistic regression model. Since most explanatory variables under consideration were correlated, we proposed the final model after backward elimination process.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.8
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• pp.596-600
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• 2009

To support an efficient management of software verification and validation activities, many defect prediction models have been proposed based on object oriented metrics. They usually adopt logistic regression analysis, And, they state that the correctness of prediction is about 60${\sim}$70%, We performed a similar experiment with Eclipse 3.3 to check their prediction effectiveness, However, the result shows that correctness is about 40% which is much lower than the original results. We also found that univariate logistic regression analysis produces better results than multivariate logistic regression analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.192-192
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• 2020

취약성 분석의 결과로 폭설에 의한 기후노출은 현재에는 강원권이 가장 취약한 것으로 나타났다. 그러나 미래에는 강원권, 충청권, 호남권을 연결하는 축으로 취약지역이 확대될 것으로 전망된다. 본 연구에서는 다양한 머신러닝 기법을 이용하여 대설피해 예측을 실시하였다. 머신러닝 기법으로는 로지스틱회귀모형, 서포트벡터 머신, 의사결정트리 모형을 적용하였다. 종속변수로 대설피해액 자료를 이용하였고, 독립변수로 기상관측자료, 사회·경제적 요소를 사용하였다. 결과적으로 기존에 사용했던 다중회귀모형과 머신러닝 기법으로 예측한 예측력을 비교 및 분석하였고, 예측력이 가장 높은 머신러닝 기법을 제시하였다. 본 연구에서 대설피해 예측을 위해 사용된 예측력이 가장 높은 기법을 활용하여 대설피해를 예측한다면, 미래에 전국적으로 확대될 대설피해에 대해 효과적으로 대비할 수 있을 것으로 기대된다.

• 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 Korean Society of Transportation
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• v.22 no.1 s.72
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• pp.33-42
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• 2004

지하철역별 수요는 개통 후 경과 연도에 따라서 S자 형태로 증가한다. 즉 개통 초기에는 잠재되어 있던 지하철 수요가 시간의 경과에 따라 계속적으로 증가하다가, 개통 후 10$\sim$13년 정도가 경과하면 최대를 나타낸 후 거의 정체하는 현상을 보인다. 그러나 지금까지 지하철 수요를 추정하기 위해서 이용되었던 4단계 모형은 이러한 지하철 수요의 증가 추세를 반영할 수 없기 때문에 실제 수요와 많은 차이를 보였다. 따라서 본 연구에서는 이러한 문제를 해결해 보고자 서울시 지하철 2$\sim$8호선의 실제 수요를 토대로 지하철역별 수요, 특히 순수한 승차인원을 추정하는 모형을 개발하였다. 모형에 적용되는 함수식은 실제 지하철역별 수요와 가장 유사한 형태를 보이고 있는 로지스틱 함수식을 이용하였다. 또한 각각의 지하철역별로 나타나는 상이한 특성은 카테고리로 분류하여 모형에 반영하였다. 카테고리는 토지이용도, 사회경제활동의 규모, 그리고 지하철역의 특성에 따라 분류하였다. 각 카테고리별 특성을 대표하는 독립 변수로 인구 종사자수, 학생수와 개통 후 경과 연도 등을 선정하였다. 그 결과 카테고리별로 추정된 지하철역별 수요는 통계적으로 매우 유의한 것으로 나타났다. 본 연구는 지하철역별로 승차하는 순수한 수요를 보다 정확하게 추정하기 위한 모형을 개발하는 것이 주된 목적이다. 반면에 본 모형을 이용하여 지하철역별 하차 수요 및 횐승 수요를 추정하는 것은 어렵다. 따라서 기존에 지하철 수요를 추정하는 데에 가장 많이 사용된 4단계 모형과 접목하여야 하며, 이에 대한 방안도 본 연구에서 제시하였다.

• Journal of Korean Society of Transportation
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• v.27 no.3
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• pp.149-160
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• 2009

The purpose of this study is to comparatively analyse the behavior patterns of the first and the repeated offenders in DWI, and to develope the models of BAC(Blood Alcohol Concentration) by using multiple regression analysis method and a model of repeated DWI conviction by using logistic regression analysis method. The main results are as follows. First, the repeated offenders are more in criminal and traffic accidents records than that of the first offenders. The unlicenced drivers are in higher BAC than licenced drivers. Second, multiple regression model of BAC was developed, and the model revealed that criminal records and driving distance were important factors. Third, a model of repeated DWI conviction was developed, and the model revealed that traffic accidents records, whether or not having licence, and criminal records were most important factors.