• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.97-100
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• 2004

We propose some properties of fuzzy multivariate analysis of variance by fuzzy vector operation with agreement index. We deals fuzzy null hypotheses and fuzzy alternative hypothesis and define the agreement index for the grades of the judgements that the hypothesis is rejection or acceptance. Finally, we provide an example to evaluate the judgements.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.44-50
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• 2024

This paper offers an overview of matrix formation and calculation techniques within the framework of General Linear Models (GLMs). It takes a sequential approach, beginning with a detailed exploration of matrix formation and calculation methods in regression analysis and univariate analysis of variance (ANOVA). Subsequently, it extends the discussion to cover multivariate analysis of variance (MANOVA). The primary objective of this study was to provide a clear and accessible explanation of the underlying matrices that play a crucial role in GLMs. Through linking, essentially different statistical methods, by fundamental principles and algebraic foundations that underpin the GLM estimation. Insights presented here aim to assist researchers, statisticians, and data analysts in enhancing their understanding of GLMs and their practical implementation in diverse research domains. This paper contributes to a better comprehension of the matrix-based techniques that can be extended to GLMs.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.363-366
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• 2004

We propose some properties of fuzzy multonomial analysis of variance by fuzzy vector operation with agreement index.

• 한국석유지질학회:학술대회논문집
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• spring
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• pp.10-14
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• 1998

A systematic methodology is developed for the electrofacies determination from wireline log data using multivariate statistical analysis. To consider corresponding contribution of each log and reduce the computational dimension, multivariate logs are transformed into a single variable through principal components analysis. Resultant principal components logs are segmented using the statistical zonation method to enhance the efficiency and quality of the interpreted results. Hierarchical cluster analysis is then used to group the segments into electrofacies. Optimal number of groups is determined on the basis of the ratio of within-group variance to total variance and core data. This technique is applied to the wells in the Korea Continental Shelf. The results of field application demonstrate that the prediction of lithology based on the electrofacies classification matches well to the core and the cutting data with high reliability This methodology for electrofacies classification can be used to define the reservoir characteristics which are helpful to the reservoir management.

• 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.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.567-578
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• 2017

Grouped multivariate data can be tested for differences between two or more groups using multivariate analysis of variance (MANOVA). However, this method cannot be used if several assumptions of MANOVA are violated. In this case, multidimensional scaling (MDS) and analysis of distance (AOD) can be applied to grouped dissimilarities based on the various distances. A permutation test is a non-parametric method that can also be used to test differences between groups. MDS is used to calculate the coordinates of observations from dissimilarities and AOD is useful for finding group structure using the coordinates. In particular, AOD is mathematically associated with MANOVA if using the Euclidean distance when computing dissimilarities. In this paper, we study the between and within group structure by applying MDS and AOD to the grouped dissimilarities. In addition, we propose a new test statistic using the group structure for the permutation test. Finally, we investigate the relationship between AOD and MANOVA from dissimilarities based on the Euclidean distance.

• Korean Journal of Health Education and Promotion
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• v.21 no.2
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• pp.147-165
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• 2004

Injury is a leading cause of death in the children and adolescent populations. In particular, more than 80％ of unintentional injury was related to risk-taking behaviors involved in diverse accidents around school and home. Therefore, educational approaches should be provided for children and adolescent populations, and schools are the essential and appropriate sites to conduct safety education. This study was conducted to identify injury prevalence and safety education at schools among middle and high school students in Korea. About 1,034 middle and high students in 28 schools participated in a self-administered survey. The target schools were selected from the stratified random sampling method throughout schools of seven metropolitan cities in Korea. The questionnaires were delivered to the vice-principals by ground mailing service and the vice-principals administered survey data collection. The questionnaire asked about safety education provided in schools, injury experience in the last year, needs for injury prevention class in school, and demographics. All survey responses were entered into SPSS worksheet. Multivariate analysis of variance (MANOVA) and descriptive discriminant analysis (DDA) were used in statistical analysis with SPSS software 11.1. Multivariate analysis of variance was conducted as a preliminary analysis of DDA. According to the result of multivariate analysis of variance, gender (man), grade (poor), living with both parents, and displaying injury prevention messages on school news board were significantly different between the injured student group and the uninjured student group (p＝ .00). These four factors also had significant effects on students' injury experience in DDA, although correlation of the four factors with injury experience was weak overall based on their canonical function coefficients. All structure coefficients of the four factors were greater than .30, which means the four factors have discriminant effects on injury prevalence. The sizes of the discriminant effects, in order, were largly from gender, grade, living with both parents, and safety message display on school news boards.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.475-485
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• 2007

In this work we measured and analyzed the subjective opinions of consumers towards the sound quality of car audios through a questionnaire. First of all, we chose eight controllable factors which had been known to affect the quality of reproduced sound. An orthogonal design of experiments was used to imitate the objective sound environments by reproducing the combinations of 8 sound characteristics, each with two levels. Then we defined 8 corresponding response variables to measure the subjective opinions towards the quality of reproduced sound. Finally, we applied the Multivariate Analysis of Valiance to explore the significant sound characteristics which affected the subjective opinions towards the quality of reproduced sound.

• Journal of Korean Society of Transportation
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• v.33 no.4
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• pp.368-378
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• 2015

This study aims to analyse the differences of job satisfaction in road freight transportation industry workers by different types of employment. The researchers utilized reliability test and factor analysis to estimate the validity and feasibility of the questionnaire. Multivariate analysis of variance (MANOVA) was also applied to assess the differences of job satisfaction level by different employment types. The results of reliability test and factor analysis clearly show that questionnaire samples are reliable and feasible. The multivariate analysis of variance result shows statistical insignificance in the level of job satisfaction between part-time workers and special type ones. On the other hand, there was a significant difference between full-time workers and those in other types of employment. The significant variables such as income, welfare, and working hour, etc were discovered.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.31-34
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• 1996

The extensive researches have compared the performance of neural networks(NN) with those of various statistical techniques for the classification problem. The empirical results of these comparative studies have indicated that the neural networks often outperform the traditional statistical techniques. Moreover, there are some efforts that try to combine various classification methods, especially multivariate discriminant analysis with neural networks. While these efforts improve the performance, there exists a problem violating robust assumptions of multivariate discriminant analysis that are multivariate normality of the independent variables and equality of variance-covariance matrices in each of the groups. On the contrary, cluster analysis alleviates this assumption like neural networks. We propose a new approach to classification problems by combining the cluster analysis with neural networks. The resulting predictions of the composite model are more accurate than each individual technique.