• Communications for Statistical Applications and Methods
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• v.29 no.4
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• pp.413-420
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• 2022

In this study we adapt discrete weibull regression model for clustered count data. Discrete weibull regression model has an attractive feature that it can handle both under and over dispersion data. We analyzed the eighth Korean National Health and Nutrition Examination Survey (KNHANES VIII) from 2019 to assess the factors influencing the 1 month outpatient stay in 17 different regions. We compared the results using clustered discrete Weibull regression model with those of Poisson, negative binomial, generalized Poisson and Conway-maxwell Poisson regression models, which are widely used in count data analyses. The results show that the clustered discrete Weibull regression model using random intercept model gives the best fit. Simulation study is also held to investigate the performance of the clustered discrete weibull model under various dispersion setting and zero inflated probabilities. In this paper it is shown that using a random effect with discrete Weibull regression can flexibly model count data with various dispersion without the risk of making wrong assumptions about the data dispersion.

• Journal of Environmental Health Sciences
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• v.23 no.3
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• pp.56-65
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• 1997

Chemical analysis, statistical analysis and geochemical study were carried out to investigate the influence of the geology on the chemical characferistics of the mineral water in Taegu area. A simple comparision between the chemical components of the mineral water and their bedrocks indicates that the bedrock types in the catchmerit area control the chemical characteristics of the surface water. However more objective evidences for the mineral water-bedrock relationship come from the statistical analyses(cluster analysis and factor analysis). The results of the statistical analyses suggest that the bedrock type factor explains the data variation seven times as much as pollution does, which evidently indicates that the bedrock in the study area mainly control the mineral water chemistries. The results of comparision of the statistical analyses results with the mineral weathering reactions and mineral stability diagrams can be summarized as follows: 1. Plagioclase weathering to kaolinite provides SiO$_2$ , Ca$^{2+}$ and Na$^+$, and muscovite weathering to kaolinite provides K$^+$, and amphibole and mica minerals weathering to kaolinite provides F to the mineral water. Most of Ca$^{2+}$ and Mg$^{2+}$ in the mineral water are the products of carbonate mineral dissolution. SO$_4^{2-}$ may be the byproduct of sulfide oxidation. 2. The weatering of silicate mineral produces Ca-rich smectite and kaolinite, but Ca-rich smectite is unstable and will be transformed to more stable kaolinite because of the continuous dilution of the mineral water by precipitation. By Hashimoto's Mineral Balance Index, S-10 and S-12 mineral spring water were evaluated tasty and healthy water, S-9 and S-11 mineral spring water were evaluated tasty water and S-7, S-8 and S-13 mineral spring water were evaluated healthy water.

• Science of Emotion and Sensibility
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• v.20 no.2
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• pp.149-160
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• 2017

In hypothesis testing, the interpretation of a statistic obtained from the data analysis relies on a probabilistic distribution of the statistic constructed according to several statistical theories. For instance, the statistical significance of a mean difference between experimental conditions is determined according to a probabilistic distribution of the mean differences (e.g., Student's t) constructed under several theoretical assumptions for population characteristics. The present study explored the logic and advantages of random-resampling approach for analyzing event-related potentials (ERPs) where a hypothesis is tested according to the distribution of empirical statistics that is constructed based on randomly resampled dataset of real measures rather than a theoretical distribution of the statistics. To motivate ERP researchers' understanding of the random-resampling approach, the present study further introduced a specific example of data analyses where a random-permutation procedure was applied according to the random-resampling principle, as well as discussing several cautions ahead of its practical application to ERP data analyses.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.4
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• pp.15-25
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• 2005

As implied by the terms of IT productivity Paradox, measuring the Information technology contribution to economic performance has been one of the challenging issues to both policy makers and business professionals. As such, diverse attempts with sophisticate analyses have been reported in the literature to analyze the effect of IT contributions. In this paper, we follow Growth Accounting Method to measure the IT contribution effect to manufacturing firm's economic performance in Korea. Various regression methods and statistical analyses are applied with fourteen years of industry Panel data. Using the Cobb-Douglas function, time lag analysis is made to understand IT effect to economic growth. Instead of capturing data from individual firm, industry level data from the National Statistics Bureau is used for IT capital, non-IT capital, and so on. Statistical analysis following the panel unit test and Panel co-integration test was performed to reveal the exact effect of IT contribution to economic performance. Empirical testing results for non-stationary nature of IT investment effect are reported as well as IT contribution to manufacturing industry's economic performance.

• Communications for Statistical Applications and Methods
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• v.25 no.3
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• pp.263-274
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• 2018

One of the common issues in large dataset analyses is to detect and construct homogeneous groups of objects in those datasets. This is typically done by some form of clustering technique. In this study, we present a divisive hierarchical clustering method for two monothetic characteristics of histogram data. Unlike classical data points, a histogram has internal variation of itself as well as location information. However, to find the optimal bipartition, existing divisive monothetic clustering methods for histogram data consider only location information as a monothetic characteristic and they cannot distinguish histograms with the same location but different internal variations. Thus, a divisive clustering method considering both location and internal variation of histograms is proposed in this study. The method has an advantage in interpreting clustering outcomes by providing binary questions for each split. The proposed clustering method is verified through a simulation study and applied to a large U.S. house property value dataset.

• Journal of Korean Society for Quality Management
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• v.14 no.1
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• pp.19-25
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• 1986

This paper studies the computer programming of statistical methods. A few computer programs are developed for * computing the basic statistics and the coefficients of process capability for raw and grouped data * drawing the frequency table and histogram * goodness of fit testing for normality with the analyses for stratifications if necessary. A special emphasis is laid on the significant digits and rounding-off for the output. A running result appears in the Appendix for a hypothetical example.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.241-252
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• 2005

In this study we suggest that the spatial correlation structure of the brain fMRI data be used to characterize the functional connectivity of the brain. For some concussion and recovery data, we examine how the correlation structure changes from one step to another in the data analyses, which will allow us to see the effect of each analysis to the spatial correlation or the functional connectivity of the brain. This will lead us to spot the processes which cause significant changes in the spatial correlation structure of the brain. We discuss whether or not we can decompose correlation matrices in terms of its causes of variations in the data.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05b
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• pp.1195-1204
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• 2002

The impacts of El Nino Southern Oscillation (ENSO) phenomenon on climate are widespread and extend far beyond the tropical Pacific. The phenomenon can be characterized by Southern Oscillation Index (SOI) which is derived from values of the monthly mean sea level pressure barometric difference between Tahiti and Darwin, Australia. Its best-known extreme is the El Nino event. In this study, general statistical characteristics of SOI and the data from which it is derived (i.e. mean sea level pressure data at Tahiti and Darwin) are presented as guidance when using SOI far other analyses. The characteristics include the availability of the barometric pressure data, statistics of monthly pressure data, correlation of SO intensity, frequency analysis of SOI by magnitude and by month (January-December), duration properties of SOI by run analysis.

• The Journal of Information Technology and Database
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• v.6 no.2
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• pp.87-100
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• 1999

This paper proposes analyses of characteristics and the technical components of Korean enterprise network. Based on a survey from professionals of Korean companies, we present statistical summaries of building blocks of hardware and software of the networks implemented in Korean companies. We also perform statistical analyses to find the relationships among the technical components and to extract major technical factors that differentiate enterprise networks by the business types and the size of the companies. We conclude that some of the technical factors are closely correlated and some of them are found to differentiate networks by the business types and the size of the companies.

• The Korean Journal of Ecology
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• v.27 no.3
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• pp.127-131
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• 2004

Soft shores are particularly vulnerable to human exploitation; however, they exhibit a variety of habitats which provide refuge for a diversity of flora and fauna. This study describes a survey of 13 soft shores in Hong Kong with information on species diversity, sediment characteristics, shore extent, pollution threat, degree of naturalness, linkage with other ecological habitats, and degree of social/economic importance. Data collected were subjected to multivariate statistical analyses, so as to identify shores that have significant ecological status and conservation value for management purposes.