• 대한교통학회지
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• 제5권1호
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• pp.25-31
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• 1987

This article shows that, in the logit models, the(conditional) expected utility of the decision makers choosing an alternative is invariant across all alternatives. This property of the logit model implies that the logit model can not explain the distributional wealfare effects of a transportation policy (or transportation investment) among different alternatives, and thus the logit model is not proper for evaluating transportation policy in equity aspects.

• Journal of the Korean Data and Information Science Society
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• 제19권3호
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• pp.735-749
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• 2008

This article suggests application of logit model for analysis of multicategory responses. Referring to the reference category, characteristic of each category is obtained from analysis of polytomous logit model. With National Pension data it is illustrated that application of logit model helps it possible to find significant factors which may not be found only with polytomous logit model. Application of the logit model is done by reducing the number of categories. Categories are grouped into the former and the latter group according to reference category. Extra finding of significant factor was possible from logistic regression analysis for the two groups after removing the reference category. It is expected that this application would be helpful for finding information and characteristics on ordered multicategory responses where the proportional odds model does not fit.

• 한국조경학회지
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• 제22권3호
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• pp.1-12
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• 1994

This study was carried out to identify destination choice behavior for one-day use recreation areas. Previous positioning study was utilized to select 4 study areas, and the secondary data were used for logit analyses. The Hausamn-McFadden test for IIA was conducted to examine whether conditional logit models are valid methodology for this study. The results revealed that IIA assumption among the study areas was violated; therefore, generalized binomial and generalized multinomial logit models were used in this study. In the binomial logit analysis, 2 to 5 independent variables were included in the models: their $\rho$2 values were from 0.1to 0.323, and accuracy of predictions were from 65.38 to 79.86 percent. In the multinomial logit analysis, 4 independent variables were included in the model: its $\rho$2 value was 0.207, and accuracy of prediction was 45.82 percent. The results showed that the conditional logit should be used with caution because of the IIA assumption. Several suggestions were described, mainly due to utilization of the secondary data for this study.

• Bulletin of the Korean Chemical Society
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• 제16권2호
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• pp.110-112
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• 1995

A LOGIT transformation is a method to estimate IC50 values with two arbitrary concentration data when complete dose response curves(DRCs) are not available. We propose a modified LOGIT transformation (MLOGIT) which predicts IC50 values more accurately than the conventional LOGIT method.

• 대한교통학회지
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• 제26권1호
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• pp.113-126
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• 2008

비확률변수간 선형관계로 정의되는 다중공선성은 설명변수간 선형방정식으로 표현되는 회귀모형의 신뢰도를 저하시키기 때문에 회귀모형의 구축과정에서는 세심한 검토와 대응이 이루어진다. 본 연구에서는 구조화된 수치실험을 통해서 로짓모형에 대한 다중공선성의 영향을 규명하였다. 효용함수를 구성하는 설명변수들간 상관관계의 정도에 따라서 추정된 모형의 적합도 지표와 계수의 신뢰도 지표가 어떻게 변동하는 지를 추적함으로써 다음과 같은 시사점을 확인할 수 있었다. 첫째, 설명변수의 추가를 통해서 모델의 적합도 개선이 가능한 회귀모형과 달리, 로짓모형에서는 효용함수에 설명변수를 추가하는 경우 로짓모형의 적합도가 개선될 수도, 역으로 저하될 수도 있음이 확인되었다. 둘째, 공통의 계수를 갖도록 모델을 구성하면 제네릭 변수간 상관관계가 높아짐에 따라 모델의 적합도가 저하됨을 확인하였다. 셋째, 설명 변수간 상관관계가 높은 경우 선택행동에 대한 설명변수의 기여도가 과대평가될 가능성을 확인하였다. 넷째, 설명변수간 상관관계가 높으면 추정된 계수의 신뢰도가 저하됨을 확인하였다. 결론적으로 본 연구를 통해서 그동안 로짓모형의 구축과정에서는 주목받지 못했던 다중공선성이 실제로는 세심한 배려와 적절한 대응을 통해서 제어되어야 함이 규명되었다.

• Communications for Statistical Applications and Methods
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• 제10권3호
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• pp.997-1005
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• 2003

We can use the logistic model for categorical data when the response variables are binary data. In this paper we consider the problem of constructing the confidence intervals for logistic model in I${\times}$J${\times}$2 contingency table. These constructions are simplified by applying logit transformation. This transforms the problem to consider linear form which called the logit model. After obtaining the confidence intervals for the logit model, the reverse transform is applied to obtain the confidence intervals for the logistic model.

• 분석과 대안
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• 제5권1호
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• pp.3-24
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• 2021

This paper introduces a multilevel item-response-theory (IRT) model as a unifying model for hypothesis testing using legislative voting data. This paper shows that a probit or logit model is a special type of multilevel IRT model. In particular, it is demonstrated that, when a probit or logit model is applied to multiple votes, it makes unrealistic assumptions and produces incorrect coefficient estimates. The advantages of a multilevel IRT model over a probit or logit model are illustrated with a Monte Carlo experiment and an example from the U.S. House. Finally, this paper provides a practical guide to fitting this model to legislative voting data.

• 유통과학연구
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• 제17권11호
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• pp.73-79
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• 2019

Purpose: The purpose of this study was evaluated to compare the predictive power of distress prediction models by using discriminant analysis method and logit analysis method for food service franchise industry in Korea. Research design, data and methodology: Forty-six food service franchise industry with high sales volume in the 2017 were selected as the sample food service franchise industry for analysis. The fourteen financial ratios for analysis were calculated from the data in the 2017 statement of financial position and income statement of forty-six food service franchise industry in Korea. The fourteen financial ratios were used as sample data and analyzed by t-test. As a result seven statistically significant independent variables were chosen. The analysis method of the distress prediction model was performed by logit analysis and multiple discriminant analysis. Results: The difference between the average value of fourteen financial ratios of forty-six food service franchise industry was tested through t-test in order to extract variables that are classified as top-leveled and failure food service franchise industry among the financial ratios. As a result of the univariate test appears that the variables which differentiate the top-leveled food service franchise industry to failure food service industry are income to stockholders' equity, operating income to sales, current ratio, net income to assets, cash flows from operating activities, growth rate of operating income, and total assets turnover. The statistical significances of the seven financial ratio independent variables were also confirmed by logit analysis and discriminant analysis. Conclusions: The analysis results of the prediction accuracy of each distress prediction model in this study showed that the forecast accuracy of the prediction model by the discriminant analysis method was 84.8% and 89.1% by the logit analysis method, indicating that the logit analysis method has higher distress predictability than the discriminant analysis method. Comparing the previous distress prediction capability, which ranges from 75% to 85% by discriminant analysis and logit analysis, this study's prediction capacity, which is 84.8% in the discriminant analysis, and 89.1% in logit analysis, is found to belong to the range of previous study's prediction capacity range and is considered high number.

• 한국ITS학회:학술대회논문집
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• 한국ITS학회 2008년도 제7회 추계학술대회 및 정기총회
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• pp.25-28
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• 2008

• 가정과삶의질연구
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• 제10권1호통권19호
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• pp.131-138
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• 1992

Regression analysis has become a standard statistical tool in the behavioral science. Because of its widespread popularity. regression has been often misused. Such is the case when the dependent variable is a qualitative measure rather than a continuous, interval measure. Regression estimates with a qualitative dependent variable does not meet the assumptions underlying regression. It can lead to serious errors in the standard statistical inference. Logit model is recommended as alternatives to the regression model for qualitative dependent variables. Researchers can employ this model to measure the relationship between independent variables and qualitative dependent variables without assuming that logit model was derived from probabilistic choice theory. Coefficients in logit model are typically estimated by the method of Maximum Likelihood Estimation in contrast to ordinary regression model which estimated by the method of Least Squares Estimation. Goodness of fit in logit model is based on the likelihood ratio statistics and the t-statistics is used for testing the null hypothesis.