• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1152-1152
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• 2001

Food adulteration is a serious consumer fraud and a matter of concern to food processors and regulatory agencies. A range of analytical methods have been investigated to facilitate the detection of adulterated or mis-labelled foods & food ingredients but most of these require sophisticated equipment, highly-qualified staff and are time-consuming. Regulatory authorities and the food industry require a screening technique which will facilitate fast and relatively inexpensive monitoring of food products with a high level of accuracy. Near infrared spectroscopy has been investigated for its potential in a number of authenticity issues including meat speciation (McElhinney, Downey & Fearn (1999) JNIRS, 7(3), 145 154; Downey, McElhinney & Fearn (2000). Appl. Spectrosc. 54(6), 894-899). This report describes further analysis of these spectral sets using a hierarchical approach and binary decisions solved using logistic regression. The sample set comprised 230 homogenized meat samples i. e. chicken (55), turkey (54), pork (55), beef (32) and lamb (34) purchased locally as whole cuts of meat over a 10-12 week period. NIR reflectance spectra were recorded over the wavelength range 400-2498nm at 2nm intervals on a NIR Systems 6500 scanning monochromator. The problem was defined as a series of binary decisions i. e. is the meat red or white\ulcorner is the red meat beef or lamb\ulcorner, is the white meat pork or poultry\ulcorner etc. Each of these decisions was made using an individual binary logistic model based on scores derived from principal component or partial least squares (PLS1 and PLS2) analysis. The results obtained were equal to or better than previous reports using factorial discriminant analysis, K-nearest neighbours and PLS2 regression. This new approach using a combination of exploratory and logistic analyses also appears to have advantages of transparency and the use of inherent structure in the spectral data. Additionally, it allows for the use of different data transforms and multivariate regression techniques at each decision step.

• 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.

• Journal of Digital Convergence
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• v.15 no.5
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• pp.487-494
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• 2017

The purpose of this study was to identify factors which predict problem drinking on adults. Using the data on the Korea Welfare Panel Study for the 7th year, 3,915 people responded to the demographic factor, psychosocial factors and drinking behavior. And the logistic regression analysis was conducted to identify predictors of problem drinking. As a result, 36 percent of those surveyed showed that the problem drinking group. Gender, age, education, occupation, economic status, self-esteem, depression, and satisfaction of family and social relationships were correlated to alcohol use. In addition, the results of logistic regression, gender, age, education, job, self-esteem, depression were predicted problem drinking. Based on these findings, it is recommended practical counterplan that prevention of the problem drinking.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.157-165
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• 2016

Among high-tech products, the mobile phone has experienced a rapid rate of innovation and a shortening of its product life cycle. The shortened product life cycle poses major challenges to those involved in the creation of forecasting methods fundamental to strategic management and planning systems. This study examined whether the best model applies to the entire diffusion life span of a mobile phone. Mobile phone sales data from a specific mobile service provider in Korea from March of 2013 to August of 2014 were analyzed to compare the performance of two S-shaped diffusion models and two non-linear regression models, the Gompertz, logistic, Michaelis-Menten, and logarithmic models. The experimental results indicated that the logistic model outperforms the other three models over the fitted region of the diffusion. For forecasting, the logistic model outperformed the Gompertz model for the period prior to diffusion saturation, whereas the Gompertz model was superior after saturation approaches. This analysis may help those estimate the potential mobile phone market size and perform inventory and order management of mobile phones.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.121-129
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• 2020

The increase of impervious surface and development along the river due to urbanization not only causes an increase in the number of associated flood risk factors but also exacerbates flood damage, leading to difficulties in flood management. Flood control measures should be prioritized based on various geographical information in urban areas. In this study, a probabilistic flood hazard assessment was applied to flood-prone areas near an urban river. Flood hazard maps were alternatively considered and used to describe the expected inundation areas for a given set of predictors such as elevation, slope, runoff curve number, and distance to river. This study proposes a Bayesian logistic regression-based flood risk model that aims to provide a probabilistic risk metric such as population-at-risk (PAR). Finally, the logistic regression model demonstrates the probabilistic flood hazard maps for the entire area.

• International Journal of Human Ecology
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• v.15 no.2
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• pp.43-57
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• 2014

Consumers differ with respect to the level of green attitudes and green purchase behavious and different consumer would have different perceptions on green market environment. This study attempted to explain how consumers perceive green market environment differently across different consumer groups in attitude-behaviour dimension in green consumption. After identifying the four consumer groups based on their attitude toward green purchase and green purchase behaviours, a multinomial logistic analysis and a stepwise discriminant analysis were conducted. This study found that reliability in green market was the most critical factor that contributes to enlarge positive green consumers. Also, the role of reference persons and adequate price of green products were also found to be important to stimulate green buying. By understanding the different role of those factors in each group of consumers, this study provided group-specific implications to expand green consumers.

• Journal of Family Resource Management and Policy Review
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• v.4 no.1
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• pp.1-13
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• 2000

This study examined patterns and determinants of families’supplementary educational investment on children. By supplementary educational investment, it meant the amounts of money spent on children’s education other than regular formal schooling expenses. The data used were from the 「1996 Household Expenditure Survey」conducted by the National Statistical Office. The statistical methods employed were descriptive statistics, cluster analysis and logistic multiple regression analysis. Results of cluster analysis revealed five different patterns of family supplementary education expense with relatively even proportion of families allocated to each pattern. The five education expenditure patterns were arts education dominant; other education dominant; gymnastics·clerical·computer education dominant; college entrance exam preparation dominant; and private tutoring dominant. Results of logistic regression analysis showed that the possibility of being in a pattern affected by various family socioeconomic variables. Important factors affecting there patterns were children’s schooling stage, residence, and mother’s education.

• Journal of Industrial Convergence
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• v.15 no.1
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• pp.17-32
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• 2017

The purpose of this study is to evaluate financial models that can predict corporate bankruptcy with diverse studies on evaluation models. The study uses discriminant analysis, logistic model, decision tree, neural networks as analyses tools with 18 input variables as major financial factors. The study found meaningful variables such as current ratio, return on investment, ordinary income to total assets, total debt turn over rate, interest expenses to sales, net working capital to total assets and it also found that prediction performance of suggested method is a bit low compared to that in literature review. It is because the studies in the past uses the data set on the listed companies or companies audited from outside. And this study uses data on the companies whose credibility is not verified enough. Another finding is that models based on decision tree analysis and discriminant analysis showed the highest performance among many bankruptcy forecasting models.

• Journal of Korean Society of Transportation
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• v.30 no.4
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• pp.85-94
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• 2012

Modern freight transport pursues not only the reduction of logistic costs but also aims at green logistics and efficient shipments. In order to accomplish these goals, various policies regarding the multimodal shipment and stopover to logistic facilities have widely been made. Such situation requires changes in existing methods for analyzing freight demand. However, the reality is that a reliable freight demand forecast is limited, since in the transport research field there is no robust freight demand model that can accommodate transshipments at logistic facilities. This study suggested a novel method to analyze freight demand, which can consider transshipments in multi-modal networks. Also, the applicability of this method was discussed through an example test.

• Communications for Statistical Applications and Methods
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• v.9 no.3
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• pp.853-864
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• 2002

In the analysis of categorical data subject to misclassification errors, the observed cell proportions are adjusted by a misclassification probabilities and estimates of variances are adjusted accordingly. In this case, it is important to determine the extent to which misclassification probabilities are homogeneous within a population. This paper considers methods to evaluate the power of chi-squared tests for homogeneity with complex survey data subject to misclassification errors. Two cases are considered: adjustment with homogeneous misclassification probabilities; adjustment with heterogeneous misclassification probabilities. To estimate misclassification probabilities, logistic regression method is considered.