• International journal of advanced smart convergence
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• v.13 no.2
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• pp.94-102
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• 2024

This study examines the factors influencing loyalty to cultural arts events using data from the Survey Report on National Culture and Arts Activity in 2022 and 2023. The dependent variable is a binary variable representing the intention to revisit in the future, which serves as a proxy for loyalty. Given that the dependent variable is binary, the logit model specification is employed to estimate the average marginal effects. The estimation results indicate that audience satisfaction exerts the strongest influence on loyalty in both years. It can be observed that participation in cultural arts events is the second most important variable in determining loyalty. This suggests that the government should support the expansion of the scope of these activities and the diversification of programs in order to facilitate greater participation in a wider range of cultural arts activities.

• Journal of the Korean Statistical Society
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• v.12 no.1
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• pp.24-35
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• 1983

In fitting models to data, selection of the most desirable estimation method and determination of the adequacy of fitted model are the central issues. This paper compares the maximum likelihood estimators and the minimum logit chi-square estimators, both being best asymptotically normal, when logit models are fitted to infant mortality data. Chi-square goodness-of-fit test and likelihood ratio one are also compared. The analysis infant mortality data shows that the outlying observations do not necessarily result in the same impact on goodness-of-fit measures.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.1-13
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• 2002

In this paper, we compares the business failure prediction accuracy among Linear Programming Discriminant Analysis(LPDA) model, Multivariate Discriminant Analysis (MDA) model and logit analysis model. The Data for 417 companies analyzed were gathered from KIS-FAS Published by Korea Information Service in 1999. The result of comparison for four time horizons shows that LPDA Is advantageous in prediction accuracy over the other two models when over all tilt ratio and business failure accuracy are considered simultaneously.

• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.2
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• pp.318-321
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• 2011

In this study, when a physician make a diagnosis of the Pattern Identifications(PIs) of stroke patients, the development methods of the PIs classification function is considered by diagnostic questionnaire of the PIs for stroke patients. Clinical data collected from 1,502 stroke patients who was identically diagnosed for the PIs subtypes diagnosed by two clinical experts with more than 3 years experiences in 13 oriental medical hospitals. In order to develop the classification function into PIs using the 44 items-Fire&heat(19), Qi-deficiency(11), Yin-deficiency(7), Dampness phlegm(7)- of them was significant statistically by univariate analysis in 61 questionnaires totally, we make some comparisons of the results of discriminant analysis model and generalized logit model. The overall diagnostic accuracy rate of the PIs subtypes for discriminant model(74.37%) was higher than 3% of generalized logit model(70.09%).

• Health Policy and Management
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• v.9 no.2
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• pp.1-20
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• 1999

This study was conducted to investigate the predictors of hospital bankruptcy in Korea and to examine the predictive power for 3 types of statistical models of hospital bankruptcy. Data on 17 financial and 4 non-financial indicators of 30 bankrupt and 30 profitable hospitals in 1. 2, and 3 years before bankruptcy were obtained from the hospital performance databank of Korea Institute of Health Services Management. Significant variables were identified through mean comparison of each indicator between bankrupt and profitable hospitals, and the predictive power of statistical models of hospital bankruptcy were compared. The major findings are as follows. 1. Nine out of 21 indicators - fixed ratio, quick ratio, operating profit to total assets, operating profit to gross revenue, normal profit to total assets,normal profit to gross revenue, net profit to gross revenue, inventories turnrounds, and added value per adjusted patient - were found to be significantly predictitive variables in Logit and Probit models. 2. The predicdtive power of discriminant model of hospital bankruptcy in 1. 2, and 3 years before bankruptcy were 85.4, 79.0, and 83.8% respectively. With regard to the predictive power of the Logit model of hospital bankruptcy, they were 82.3, 75.8, and 80.6% respectively, and of the Probit model. 87.1. 80.6, and 88.7% respectively. 3. The predictive power of the Probit model of hospital bankruptcy is better than the other two predictive models.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.15-22
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• 2021

This study aims to obtain a stock investment strategy model based on the industrial sector in Indonesia Stock Exchange (IDX). This study uses IDX data for the period of January 1996 to December 2016. This study uses the Markov Regime Switching Model to identify trends in market conditions that occur in industrial sectors on IDX. Furthermore, by using the Logit Regression Model, we can see the influence of economic factors in determining trends in market conditions sectorally and the probability of trends in market conditions. This probability can be the basis for determining stock investment decisions in certain sectors. The results showed descriptively that the stocks of the consumer goods industry sector had the highest average return and the lowest standard deviation. The trend in sectoral stock market conditions that occur in IDX can be divided into two conditions, namely bullish condition (high returns and low volatility) and bearish condition (low returns and high volatility). Differences in the conditions are mainly due to differences in volatility. The use of a Logit Regression Model to produce probability of market conditions and to estimate the influence of economic factors in determining stock market conditions produces models that have varying predictive abilities.

• Journal of Dairy Science and Biotechnology
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• v.41 no.1
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• pp.1-8
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• 2023

Response surface methodology (RSM) is a statistical approach widely used in food processing to optimize the formulation, processing conditions, and quality of food products. The homogenization process is achieved by subjecting milk to high pressure, which breaks down fat globules and disperses fat more evenly throughout milk. This study focuses on an application of RSM including the logit transformation to predict the efficiency of milk homogenization, which can be maximized by minimizing the relative difference in fat percentage between the top part and the remainder of milk. To avoid a negative predicted value of the minimum of this proportion, the logit transformation is used to turn the proportion into the logit, whose possible values are real numbers. Then, the logit values are modeled and optimized. Subsequently, the logistic transformation is used to turn the predicted logit into the predicted proportion. From our model, the optimum condition for the maximized efficiency of milk homogenization was predicted as the combination of a homogenizer pressure of 30 MPa, a storage temperature of 10℃, and a storage period of 10 days. Additionally, with a combination of a homogenizer pressure of 30 MPa, a storage temperature of 10℃, and a storage period of 50 days, the level of milk homogenization was predicted to be acceptable, even with the problem of extrapolation taken into account.

• The Korean Journal of Applied Statistics
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• v.23 no.6
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• pp.1057-1065
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• 2010

In this paper we discuss some cautionary notes in using the Pearson chi-squared test statistic for the goodness-of-fit of the ordinal response model. If a model includes continuous type explanatory variables, the resulting table from the t of a model is not a regular one in the sense that the cell boundaries are not fixed but randomly determined by some other criteria. The chi-squared statistic from this kind of table does not have a limiting chi-square distribution in general and we need to be very cautious of the use of a chi-squared type goodness-of-t test. We also study the limiting distribution of the chi-squared type statistic for testing the goodness-of-t of cumulative logit models with ordinal responses. The regularity conditions necessary to the limiting distribution will be reformulated in the framework of the cumulative logit model by modifying those of Moore and Spruill (1975). Due to the complex limiting distribution, a parametric bootstrap testing procedure is a good alternative and we explained the suggested method through a practical example of an ordinal response dataset.

• Journal of Korean Society of Rural Planning
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• v.14 no.3
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• pp.37-51
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• 2008

This study aims to build a model dealing with the location decision of new manufacturing firms and their land demand. The model is composed with 1) the binary logit model structure identifying a future probability of manufacturing firms to locate in a city and their land demand; and 2) the land use suitability of the land demand. The model was empirically tested in the case of Anseong City. We used establishment-level data for the manufacturing industry from the Report on Mining and Manufacturing Survey. 48 industry groups were scrutinized to find the location probability in the city and their land demand via logit model with the dependent variables: number of employment, land capital, building capital, total products, and value-added for a new industry since 2001. It is forecasted that the future land areas (to 2025) for the manufacturing industries in the city are $5.94km^2$ and additional land demand for clustering the existing industries scattered over the city is $2.lkm^2$. Five industrial complex locations were identified through the land use suitability analysis.

• Journal of Korean Society of Rural Planning
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• v.17 no.1
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• pp.59-65
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• 2011

Re-visitation have an effect on dependent variables of regional tourism demand model. This study focused on the re-visitation impacts of consideration on sustainable tourism development of tourists as a new factors of tourism. Based on literature reviews, 11 variables were selected, a questionnaire survey was given to 406 tourists divided into 5 tourism sites at Chuncheon city, and logit model and probit model were used for analysis. The fitness levels of two models were very significant(p=0.0000). The study results suggest that the likelihood of the rural tourist to make a return visit is influenced by recognition of sustainable tourism, purchase of souvenir and farm produce, visitation of regional shops, conversation with regional residents, residents' participation on development, age and marriage. The results of such re-visitation demand can provide information for regional development strategies. The approach to re-visitation research impacts of consideration on sustainable tourism development is expected to become a useful foundation in studying on sustainable regional development.