• Journal of the Korean Data and Information Science Society
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• v.23 no.3
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• pp.457-465
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• 2012

The purpose of this study is to analyze the association between indicators and to find statistical models based on important indicators at 'College Notifier' in Korea Council for University Education. First, Pearson correlation coefficients are used to find statistically significant correlations. By variable selection method, the important indicators are selected and their coefficients are estimated. As variable selection method, backward and stepwise methods are employed.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.42 no.5
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• pp.571-578
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• 1997

The development of new approaches for wheat grain hardness assessment may impact the grain industry in marketing, milling and breeding. This experiment was to develop a new method for fast identification between softness and hardness, and for maintaining germinability of seed after measurement in wheat. Results from the comparisons of accuracy and significance between image analysis and conventional methods(NIRS and textrometer) were summarized. Data obatined from image analysis for grain hardness did not show any difference from those of the conventional methods. The protein content analyzed by micro-Kjeldahl method was significantly correlated with the grain hardness measured by image analysis, textrometer, and NIRS. The analysis for wheat grain hardness using image analysis may be used as an alternative method to the conventional methods. This method also takes the seeds after analysis can be utilized as breeding materials in early generations.

• Proceedings of the Korean Association for Survey Research Conference
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• 2001.04a
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• pp.109.2-130
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• 2001

The purpose of this study is (1) to demonstrate that ignoring group differences may mislead to incorrect conclusion when analyzing correlation data (e.g., factor analysis), (2) to highlight the importance of the data analytic method considering group differences, and (3) to provide ways of incorporating group differences in data analysis. In study, 1, ignoring group difference in factor analysis may mislead to incorrect factor structure. To remedy this, z-transform method and group analysis tool in covariance structure models were suggested. In study 2, the group differences effect was illustrated by using real data (IQ test data).

• Journal of Digital Convergence
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• v.11 no.12
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• pp.201-211
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• 2013

This study compares Customer Satisfaction Index(CSI) and the weight for each dimension by applying various methods of weight calculation and attempts to suggest some implications. For the purpose, the study classified the methods of weight calculation into the subjective method and the statistical method. Constant sum scale was used for the subjective method, and the statistical method was again segmented into correlation analysis, principal component analysis, factor analysis, structural equation model. The findings showed that there is difference between the weights from the subjective method and the statistical method. The order of the weights by the analysis methods were classified with similar patterns. Besides, the weight for each dimension by different methods of weight calculation showed considerable deviation and revealed the difference of discrimination and stability among the dimensions. Lastly, the CSI calculated by various methods of weight calculation showed to be the highest in structural equation model, followed by in the order of regression analysis, correlation analysis, arithmetic mean, principal component analysis, constant sum scale and factor analysis. The CSI calculated by each method showed to have statistically significant difference.

• Journal of the Korean Geographical Society
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• v.30
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• pp.62-76
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• 1984

이 글에서는 다음의 내용을 다루었다. 1. 연구지역과 분석방법, (1) 연구지역, (2) 분석지표와 자료, (3) 분석방법 2. 공간적 자기상관의 측도와 검정, (1) 공간적 자기상관의 개념, (2) 공간적 자기상관의 측도, (3) 공간적 자기상관의 검정 3. 공간적 패턴의 형성과정, (1) 제1기 (1960-1966년), (2) 제2기 (1966-1970년), (3) 제3기(1970-1975년), (4) 제4기(1975-1980년)

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05a
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• pp.80-87
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• 1996

국내 가동중원전의 노물리 시험중 제어봉제어능 측정시 적용되는 측정방법들에 대해 실제 측정결과를 이용해서 통계적 분석 및 추론을 수행하였다. 1988년부터 현재까지 약 8년간 모든 발전소에서 행한 측정결과에 대해 통계적 분석 및 주요 시험변수들과의 상관관계를 분석한 결과, 다음과 같은 결론이 도출되었다. 제어봉 제어능 측정결과는 전반적으로 측정치가 예측치에 비해 작은 경향을 보이고 있다. 붕산희석법은 측정오차가 가장 작았고 임의성을 가지며, 노심의 붕산농도와 상관관계가 있다. Framatome방식의 제어봉교환법은 비교적 측정오차가 크나, 시험변수들과의 상관 관계는 가장 미약하다. CE 방식의 제어봉교환법은 Swap 방법 중에는 측정오차가 가장 작고 임의성이 있으나, 붕산농도 및 기준제어군 제어능 측정오차의 크기와 강한 상관관계를 갖는다. Westinghouse 방식의 제어봉교환법은 가장 측정오차가 크며 대부분의 측정결과에서 측정치가 작게 편향되어 있다. 그리고 시험제어군의 제어능이 작을수록 측정오차가 커지는 강한 상관관계가 있으며, 기준제어군 제어능 측정오차의 크기와도 상관관계가 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.659-662
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• 2013

과거에는 보안디바이스의 안전성을 탑재된 암호알고리즘의 안전성에 의존하였다. 하지만 부채널 분석을 통해 암호알고리즘의 안전성과는 별개로 부채널 정보에 의한 보안디바이스의 물리적 취약성이 존재함이 밝혀졌다. 이러한 보안디바이스의 물리적 취약성을 보완하기 위해서는 최소 2차 상관전력분석에 대한 대응법이 간구되어야 한다. 최근 2차 상관전력분석에 대한 실용적인 대응법으로 1차 마스킹과 셔플링을 혼합한 방법을 많이 활용하고 있다. 하지만 1차 마스킹과 셔플링이 혼합된 부채널 대응법을 1차 상관전력분석으로 분석하였을 경우, 특이한 피크가 발생한다. 본 논문에서는 마스킹과 셔플링이 혼합된 부채널 대응법을 1차 상관전력분석으로 분석하였을 때, 특이한 피트가 발생함을 실험적으로 확인하였고, 그 피크 발생 원인을 소개한다. 뿐만 아니라, 피크 발생 정보를 추가적인 부채널 분석 정보로 활용할 수 있는 방법을 소개한다.

• Journal of the Korean GEO-environmental Society
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• v.16 no.5
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• pp.43-53
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• 2015

In this study, detection methods of outlier in various monitoring data that fit into big data category were developed and outlier detections were conducted for both artificial data and real field monitoring data. Rule-based methods applied rate of change and probability of error for monitoring data are effective to detect a large-scale short faults and constant faults having no change within a certain period. There are however, problems with misjudgement that consider the normal data with a large scale variation as outlier caused by using independent single dataset. Rule-based methods for noise faults detection have a limit to application of real monitoring data due to the problem with a choice of proper window size of data and finding of threshold for outlier judgment. A correlation analysis among different two datasets were very effective to detect localized outlier and abnormal variation for short and long-term monitoring dataset if reasonable range of training data could be selected.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.157-176
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• 2024

Integrative analysis for better understanding of complex biological systems gains more attention. Observing subjects from various perspectives and conducting integrative analysis of those multiple datasets enables a deeper understanding of the subject. In this paper, we compared two methods that simultaneously consider two datasets gathered from the same objects, canonical correlation analysis (CCA) and co-inertia analysis (CIA). Since CCA cannot handle the case when the data exhibit high-dimensionality, two strategies were considered instead: Utilization of a ridge constant (CCA-ridge) and substitution of covariance matrices of each data to identity matrix and then applying penalized singular value decomposition (CCA-PMD). To illustrate CIA and CCA, both extensions of CCA and CIA were applied to NCI60 cell line data. It is shown that both methods yield biologically meaningful and significant results by identifying important genes that enhance our comprehension of the data. Their results shows some dissimilarities arisen from the different criteria used to measure the relationship between two sets of data in each method. Additionally, CIA exhibits variations dependent on the weight matrices employed.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.04a
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• pp.159-162
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• 2002

본 논문은 두 이미지간 색차 (Color Difference)를 도출하는 두 가지 방법론, 즉 정량적, 정성적 방법간의 통계적 상관관계분석에 관한 것이다. 이 연구의 목적은 두 가지 방법론에 통계적으로 유의한 상관관계가 있을 경우 관찰자를 동원하고 상대적으로 많은 자원이 소요되는 정성적 연구를 정량적 차이분석으로 대체할 수 있는가의 가능성을 분석키 위한 것이다.