• Animal Bioscience
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• v.34 no.12
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• pp.1995-2002
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• 2021

Objective: Detection of adulteration in processed meats is an important issue for some countries due to substitution of beef with a cheaper source of protein like poultry. In this study, the presence of chicken meat was investigated using real-time polymerase chain reaction (real-time PCR) and enzyme-linked immunosorbent assay (ELISA) techniques to verify adulteration of fermented sausage samples. Methods: A total of 60 commercial samples were collected from 20 establishments in three replicates including 10 fermented sausage manufacturers and 10 butchers to investigate the presence of chicken meat with the sequential use of real-time PCR and ELISA techniques. In addition, pH, moisture content, water activity and color values of the samples were determined. Results: Both real-time PCR and ELISA showed agreement on the presence or absence of chicken meat in 55 out of 60 fermented sausage samples and chicken meat was identified with both methods in 16 samples. Five samples produced inconsistent results for the presence of chicken meat in the first run. Nevertheless, the presence of chicken meat was verified with both methods when these samples were analyzed for the second time. In addition, the average physico-chemical values of the fermented sausage samples tested positive for chicken meat were not significantly different from some of those fermented sausage samples tested negative for the chicken meat. Conclusion: The sequential use of real-time PCR and ELISA techniques in fermented sausages could be beneficial for the government testing programs to eliminate false negatives for detection of adulteration with chicken meat. Furthermore, consumers should not rely on some of the quality cues including color to predict the adulteration of fermented sausages with chicken meat since there were no statistical differences among some of the samples tested positive and negative for chicken meat.

• Journal of Korean Society for Quality Management
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• v.35 no.4
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• pp.159-165
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• 2007

Using statistical methods a change-point analysis of oil supply disruption is conducted. The statistical distribution of oil supply disruption is a weibull distribution. The detection of the change-point is applied to Bayesian method and weibull parameters are estimated through Markov chain monte carlo and parameter approach. The statistical approaches to the estimation for the change-point and weibull parameters is implemented with the sets of simulated and real data with small sizes of samples.

• Communications for Statistical Applications and Methods
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• v.24 no.3
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• pp.193-209
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• 2017

The power Lindley distribution with some of its properties is considered in this article. Maximum likelihood, least squares, maximum product spacings, and Bayes estimators are proposed to estimate all the unknown parameters of the power Lindley distribution. Lindley's approximation and Markov chain Monte Carlo techniques are utilized for Bayesian calculations since posterior distribution cannot be reduced to standard distribution. The performances of the proposed estimators are compared based on simulated samples. The waiting times of research articles to be accepted in statistical journals are fitted to the power Lindley distribution with other competing distributions. Chi-square statistic, Kolmogorov-Smirnov statistic, Akaike information criterion and Bayesian information criterion are used to access goodness-of-fit. It was found that the power Lindley distribution gives a better fit for the data than other distributions.

• Journal of Korean Society for Atmospheric Environment
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• v.2 no.1
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• pp.41-54
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• 1986

Methods used for averaging PH's of rainwater and site representation have been studied, Statistical analysis was attempted regarding effects of ionic components on PH's utilizing 847 data altogether obtained in two years, 1984 and 1985. The outcome of the study may be assumarized as follows: 1. Methods for Averaging PH Volume weighted method is considered to be acceptable providing that precipitation is measured at the same time when the samples are taken. Without precipitation data a simple averaging method should be the next choice. 2. Site Representation A statistical method used for optimizing a monitoring newtork was applied using the data collected. Because of a limited number of data, no discernible conclusion can be reached suggesting that the method can serve as a good guide when the data base becomes more reliable. 3. A good correlation appears to exist betwen conductivities and ionic components in rainwater. It would, therefore, be possible to certain extend to estimate ionic concentrations from conductivity measurements by correlation equations. 4. The acidity of rainwater is effected by $SO_4^{2-}, NO_3^-, Cl^- and NH_4^+ with SO_4^{2-}$ being the most significant as demonstrated by standardized regression analysis.

• Communications for Statistical Applications and Methods
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• v.21 no.5
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• pp.395-409
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• 2014

Data limited, partial, or incomplete are known as an ill-posed problem. If the data with ill-posed problems are analyzed by traditional statistical methods, the results obviously are not reliable and lead to erroneous interpretations. To overcome these problems, we propose a dual generalized maximum entropy (dual GME) estimator for panel data regression models based on an unconstrained dual Lagrange multiplier method. Monte Carlo simulations for panel data regression models with exogeneity, endogeneity, or/and collinearity show that the dual GME estimator outperforms several other estimators such as using least squares and instruments even in small samples. We believe that our dual GME procedure developed for the panel data regression framework will be useful to analyze ill-posed and endogenous data sets.

• Korean Journal of Human Ecology
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• v.14 no.1
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• pp.47-57
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• 2005

We conducted a content analysis of the researches published for a 7-year period (1997-2003) in the Korean Journal of Play Therapy (Korean Association for Play Therapy) and The Journal of Play Therapy (Korean Association of Psychological Rehabilitation for Children). Specifically, we analyzed a total of 156 articles for research contents, instruments, subjects, statistical analytic methods, and references. The results of the study are as follows: First, the principal areas of research activity and publication were specific theoretical review, personality, and adjustment research. Most outcomes, processes, and outcome researches were conducted in a form of case study. Second, typical samples contained 4 to 6 year old children or elementary schoolers. A lot of them were counseling center clients or normally adapted children. Third, 37% of the articles used ANOVA-related statistical analytic methods, and 35% of them used descriptive statistical analytic methods. Lastly, many articles averagely cited foreign references written 11 years prior to domestic references.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.389-399
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• 2002

In order to examine whether the difference between two point estimates of population proportions is statistically significant, data analysts use two techniques. The first is to explore the overlap between two associated confidence intervals. Second method is to test the significance which is introduced at most statistical textbooks under the common assumptions of consistency, asymptotic normality, and asymptotic independence of the estimates. Under the null hypothesis which is two population proportions are equal, the pooled estimator of population proportion is preferred as a point estimator since two independent random samples are considered to be collected from one population. Hence as an alternative method, we could obtain another confidence interval of the difference of the population proportions with using the pooled estimate. We conclude that, among three methods, the overlapped method is under-estimated, and the difference of the population proportions method is over-estimated on the basis of the proposed method.

• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.547-568
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• 2020

Modeling the statistical autocorrelations in spatial data is often achieved through the estimation of the variograms, where the selection of the appropriate valid variogram model, especially for small samples, is crucial for achieving precise spatial prediction results from kriging interpolations. To estimate such a variogram, we traditionally start by computing the empirical variogram (traditional Matheron or robust Cressie-Hawkins or kernel-based nonparametric approaches). In this article, we conduct numerical studies comparing the performance of these empirical variograms. In most situations, the nonparametric empirical variable nearest-neighbor (VNN) showed better performance than its competitors (Matheron, Cressie-Hawkins, and Nadaraya-Watson). The analysis of the spatial groundwater dataset used in this article suggests that the wave variogram model, with hole effect structure, fitted to the empirical VNN variogram is the most appropriate choice. This selected variogram is used with the ordinary kriging model to produce the predicted pollution map of the nitrate concentrations in groundwater dataset.

• Journal of Korean Society for Quality Management
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• v.18 no.1
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• pp.84-95
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• 1990

The objective of this study is to understand the effect of measurement-related investment. That is, this study aims at verifying the correlation between the measurement-related investment and inferiority rate of products by statistical analysis. The samples of this study are 376 industrial companies in Korea, and the research data was analysed on inferiority state of industrial companies with a measurement standard laboratory. The analysis was made by the elementary statistics, the correlation analysis and the regression analysis. The results are summarized as follows : First, the inferioriy rate of the industrial companies with a measurement standard laboratory was relatively lower than that of the other companies without the laboratory by statistical significance. Second, the increment on measurement-related investment had a negative correlation with the increment of inferiority rate, and the increase of measurement-related investment showed decrease of the inferiority rate by regression analysis.

• Communications for Statistical Applications and Methods
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• v.14 no.3
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• pp.641-648
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• 2007

We propose the new sampling method, called modified ranked ordering set sampling (MROSS). Kim and Kim (2003) suggested the sign test using the ranked ordering set sampling (ROSS), and showed that the asymptotic relative efficiency (ARE) of ROSS against RSS for sign test increases as sample size does. We propose the estimator for the population mean using MROSS. The relative precision (RP) of estimator of the population mean using MROSS method with respect to the usual estimator using modified RSS is higher, and when the underlying distribution is skewed, the bias of the proposed estimator is smaller than that of several ranked set sampling estimators.