• Communications for Statistical Applications and Methods
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• v.3 no.1
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• pp.11-19
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• 1996

Canonical correlation analysis is a multivariate technique for identifying and quantifying the statistical relationship between two sets of variables. Like most multivariate techniques, the main objective of canonical correlation analysis is to reduce the dimensionality of the dataset. It would be particularly useful if high dimensional data can be represented in a low dimensional space. In this study, we will construct statistical graphs for paired sets of multivariate data. Specifically, plots of the observations as well as the variables are proposed. We discuss the geometric interpretation and goodness-of-fit of the proposed plots. We also provide a numerical example.

• Journal of Applied Reliability
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• v.1 no.1
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• pp.17-33
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• 2001

The characteristics of Weibull distribution are investigated as a function of shape parameter. The statistical estimation methods of the shape parameter and statistical comparison methods of two or more shape parameters are studied. Assuming Weibull distribution, statistical analysis of bending-strength data of alumina titanium carbide ceramic matrix composites machined two different methods are performed.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.243-248
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• 1995

The problem of optimal design for a nonparametric regression with binary data is considered. The aim of the statistical analysis is the estimation of a quantal response surface in two dimensions. Bias, variance and IMSE of kernel estimates are derived. The optimal design density with respect to asymptotic IMSE is constructed.

• Nuclear Engineering and Technology
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• v.30 no.4
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• pp.364-376
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• 1998

The statistical analysis method was applied to the evaluation of fracture toughness in the ductile-brittle transition temperature region. Because cleavage fracture in steel is of a statistical nature, fracture toughness data or values show a similar statistical trend. Using the three-parameter Weibull distribution, a fracture toughness vs. temperature curve (K-curve) was directly generated from a set of fracture toughness data at a selected temperature. Charpy V-notch impact energy was also used to obtain the K-curve by a $K_{IC}$ -CVN (Charpy V-notch energy) correlation. Furthermore, this method was applied to evaluate the neutron irradiation embrittlement of reactor pressure vessel (RPV) steel. Most of the fracture toughness data were within the 95% confidence limits. The prediction of a transition temperature shift by statistical analysis was compared with that from the experimental data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.169-174
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• 2018

In general, the methods of the analysis of variance(ANOVA) for the continuous data and the chi-square test for the discrete data are used for statistical analysis of the effect and the association. In multidimensional data, analysis of hierarchical structure is required and statistical linear model is adopted. The structure of the linear model requires the normality of the data. A multidimensional categorical data analysis methods are used for causal relations, interactions, and correlation analysis. In this paper, Bayesian network model using probability distribution is proposed to reduce analysis procedure and analyze interactions and causal relationships in categorical data analysis.

• Journal of Multimedia Information System
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• v.5 no.3
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• pp.195-200
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• 2018

Recently, as the sensor and big data analysis technology have been developed, there have been a lot of researches that analyze the purchase-related data such as the trajectory information and the stay time. Such purchase-related data is usefully used for the purchase pattern prediction and the purchase time prediction. Because it is difficult to find periodic patterns in large-scale human data, it is necessary to look at actual data sets, find various feature patterns, and then apply a machine learning algorithm appropriate to the pattern and purpose. Although existing papers have been used to analyze data using various machine learning methods, there is a lack of statistical analysis such as finding feature patterns before applying the machine learning algorithm. Therefore, we analyze the purchasing data of Songjeong Maeil Market, which is a data gathering place, and finds some characteristic patterns through statistical data analysis. Based on the results of 1, we derive meaningful conclusions by applying the machine learning algorithm and present future research directions. Through the data analysis, it was confirmed that the number of visits was different according to the regional characteristics around Songjeong Maeil Market, and the distribution of time spent by consumers could be grasped.

• Journal of Elementary Mathematics Education in Korea
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• v.20 no.1
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• pp.55-69
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• 2016

The purpose of this study is to investigate statistical units of elementary school mathematics textbooks upon on the statistical problem solving process to provide useful information for qualitative improvement of developing curriculum and teaching materials. This study analyzed the statistical units from the textbooks of 1st to 6th year along the 2009 revised national curriculum. The analysis frame is based on the 4 phases of the statistical problem solving process: formulate questions, plan and collect data, present and analyze data and interpret data.

• Journal of the Korean Statistical Society
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• v.25 no.1
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• pp.49-66
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• 1996

The singular value decomposition is one of the most useful methods in the area of matrix computation. It gives dimension reduction which is the centeral idea in many multivariate analyses. But this method is not resistant, i.e., it is very sensitive to small changes in the input data. In this article, we derive the resistant version of singular value decomposition for principal component analysis. And we give its statistical applications to biplot which is similar to principal component analysis in aspects of the dimension reduction of an n x p data matrix. Therefore, we derive the resistant principal component analysis and biplot based on the resistant singular value decomposition. They provide graphical multivariate data analyses relatively little influenced by outlying observations.

• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.167-180
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• 2006

The statistical survey and analysis is much restricted to time, space and material. The statistical survey and analysis could hardly resume. The statistical survey and analysis is very important to create various and accurate information. The statistical survey and analysis which is not a expert knowledge have many problems in productivity of information, reliability and etc. In this paper, we study the design and Implementation of web-based statistical survey and analysis consulting system which a client meet easily a statistical expert on the web.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2009.05a
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• pp.153-159
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• 2009

The purpose of this paper is to suggest the problems of basic population data(1960-2005) and the data(2006-2050) of population projections reported by Korean National Statistical Office in November 2006. The errors on the basic population data can be easily checked by using the graphical analysis and the method of linear regression analysis. It is necessary to revise the population projections reported by Korean National Statistical Office.