• Journal of the Korean Statistical Society
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• v.2 no.1
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• pp.47-51
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• 1973

No Abstract, See Full Text

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.431-445
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• 2006

In this paper we review some methods for analyzing functional data and illustrate real application of functional data analysis. Representing methods for functional data by using basis function, analyzing functional variation by functional principal component analysis and functional linear models are reviewed. For a real application, we use temperature and precipitation data measured in Korea from the January of 1970 to the May of 2004. We apply functional principal component analysis for each data and test the significance of regional division done by using shining hours. We also estimate functional regression model for temperature and precipitation.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.579-591
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• 2022

Functional data is collected in various fields. It is often necessary to test whether there are differences among groups of functional data. In this case, it is not appropriate to explain using the point-wise ANOVA method, and we should present not the point-wise result but the integrated result. Various studies on functional data analysis of variance have been proposed, and recently implemented those methods in the package fdANOVA of R. In this paper, I first explain ANOVA and multivariate ANOVA, then I will introduce various methods of analysis of variance for univariate and multivariate functional data recently proposed. I also describe how to use the R package fdANOVA. This package is used to test equality of weekly temperatures in Seoul and Busan through univariate functional data ANOVA, and to test equality of multivariate functional data corresponding to handwritten images using multivariate function data ANOVA.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.21-26
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• 2018

As the large amount of data can be efficiently stored, the methods extracting meaningful features from big data has become important. Especially, the techniques of converting high- to low-dimensional data are crucial for the 'Data visualization'. In this study, principal component analysis (PCA; linear dimensionality reduction technique) and Isomap (non-linear dimensionality reduction technique) are introduced and applied to neural big data obtained by the functional magnetic resonance imaging (fMRI). First, we investigate how much the physical properties of stimuli are maintained after the dimensionality reduction processes. We moreover compared the amount of residual variance to quantitatively compare the amount of information that was not explained. As result, the dimensionality reduction using Isomap contains more information than the principal component analysis. Our results demonstrate that it is necessary to consider not only linear but also nonlinear characteristics in the big data analysis.

• Journal of the Korean Society of Costume
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• v.67 no.3
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• pp.47-65
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• 2017

The purpose of this study was to divide respondents by consumption values, and to examine the differences in their cosmetic purchase behavior. Cosmetic purchase behavior consisted of variables such as purchase frequency, purchase amount, place of purchase, purchase reason, reason for using cosmetics, purchase propensity, degree of using information source, and selection criteria. A survey was conducted with 308 women between the ages 20 and 29 from December 5th to 10th 2016. Data collected from the respondents through an Internet survey were analyzed using descriptive statistics, factor analyses, cluster analysis, analyses of variance and chi-square tests. Four consumption value dimensions emerged that were termed emotional, differentiated individuality-pursuing, functional and social value. The respondents were classified into three groups(emotional consumer group, functional consumer group, active consumer group) by cluster analysis using four dimensions of consumption value. The results of the analyses of variance and chi-square tests showed significant differences in purchase frequency, place of purchase, purchase reason, reason for using cosmetics, degree of using information source and selection criteria among groups classified by consumption value. However, there were no differences in purchase amount and purchase propensity among them.

• Journal of the Korean Society of Physical Medicine
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• v.17 no.1
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• pp.11-20
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• 2022

PURPOSE: A gait assessment is an important component of the rehabilitation process, and observational gait assessment (OGA) is used routinely in clinical settings. This study examined the association of OGA tools with the independent walking ability in stroke patients to determine a cutoff value of the OGA tool according to independence levels of stroke patient gait. METHODS: Two hundred ten hemiparetic stroke patients participated in the study. The independence of gait was identified using the Functional Ambulation Category (FAC) classifications. The walking ability was assessed using OGA tools (Rivermead Visual Gait Assessment [RVGA], Wisconsin Gait Scale [WGS], Tinetti Gait Scale [TGS], and Functional Gait Analysis [FGA]). RESULTS: Stepwise multiple regression analysis showed that among the OGA tools, the FGA correlated with the FAC. The FGA explained approximately 77% of the variance in FAC. In distinguishing the independence levels, the cutoff values were as follows: between FAC 1 and FAC 0 was .5 points; between FAC 2 and lower levels, 5.5; between FAC 3 and lower levels, 11.5; between FAC 4 and lower levels, 14.5; and between FAC 5 and lower levels, 18.5. Items 1, 2, 3, and 10 were identified as explaining most of the variance in the FGA in the stepwise multiple regression. CONCLUSION: The present study found that the FGA is an assessment tool related to the level of gait independence after stroke. Furthermore, the FGA total score can serve as an index of the increase in independence level after stroke.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.72-76
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• 1990

A new statistical linearization technique for nonlinear system called covariance matching method is proposed in this paper. The covariance matching method makes the mean and variance of an approximated output be identical real functional output, and the distribution of the approximated output have identical shape with a given random input. Also, the covariance matching method can be easily implemented for statistical analysis of nonlinear systems with a combination of linear system covariance analysis.

• Culinary science and hospitality research
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• v.21 no.6
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• pp.331-340
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• 2015

This study examined the factors that affect the relationship among functional bakery purchase motivation, consumer attitudes, and purchase intention. A total of 230 questionnaires were distributed to the consumers, of which 210 were deemed suitable for analysis after the removal of 20 unusable responses. In order to perform statistical analyses required in current study, SPSS 18.0 statistical program was employed for frequency analysis, factor analysis, and reliability analysis, Correlations, regression analysis. The results of exploratory factor analysis showed that four factors regarding local specialties purchase motivation were extracted from all measurements with a KMO of 0.735 and a total cumulative variance of 65.759%, with regard to consumer attitudes, one factors were extracted with a total cumulative variance of 72.421% and a KMO score of 0.684. One factor for purchase intention was extracted that accounted for a total cumulative variance of 67.139% and a KMO score of 0.683. All factors were significant and the correlation between variables was significant as well. Thus, based on the results, the main research hypothesis that identifies the relationships purchase motivation between consumer attitudes and purchase intention were partially adopted.

• Phonetics and Speech Sciences
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• v.10 no.4
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• pp.127-134
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• 2018

This study aims to explain the difficulties children with articulation and phonological disorders have in producing alveolar fricative sounds. The study will perform a comparative analysis revealing how ordinary children produce alveolar fricative sounds through five different acoustic variables, and consequently identifying objective differences, compared to children with articulation and phonological disorders. Therefore, this study compared and analyzed the differences between 10 children with articulation and phonological disorders and 10 ordinary children according to a phonation type of alveolar fricative sounds (/s/ and /$s^*$), a type of vowel (/i/, /ε/, /u/, /o/, /ɯ/, /ʌ/, /ɑ/), and a structure of syllables (CV, VCV) through acoustic variables including a central moment, skewness, kurtosis, a center of gravity and variance. That is, children with articulation and phonological disorders, when compared to ordinary children, have difficulties with concentrating an agile and momentary friction with strength when articulating alveolar fricative sounds, which uses strong energy and accompany tension. Furthermore, the values of alveolar fricative sounds of children with articulation and phonological disorders appeared to spread evenly over the average range, which means that the range of overall the standard deviation values for children with functional phonological disorders is wider than that of ordinary children. For a future study, if the mispronounced sounds relating to omission, substitution, and addition can be compared and analyzed for various target groups, it could be used effectively to help children with functional phonological disorders.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.54 no.1
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• pp.24-28
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• 2009

This study was conducted to evaluate the growth characters and yield components of 18 collected sesame cultivars to get basic information on the variation for the sesame breeding using principal component analysis. All characters except days to flowering, days to maturity and 1,000 seed weight showed significantly different. Seed weight per 10 are showed higher coefficient of variance. Capsule bearing stem length and liter weight showed positive correlation with seed yield per 10 are. The principal components analysis grouped the estimated sesame cultivars into four main components which accounted for 83.7% of the total variation at the eigenvalue and its contribution to total variation obtained from principal component analysis. The first principal component ($Z_1$) was applicable to increase plant height, capsule bearing stem length and 1,000-seed weight. The second principal component ($Z_2$) negatively correlated with days to flowering and maturity by which it was applicable to shorten flowering and maturity date of sesame. At the scatter diagram, Yangbaek, Ansan, M1, M2, M4, M7 and M9 were classified as same group, but M10, Yanghuk, Kanghuk, M5, M6, M12 and M13 were classified as different group. This results would be helpful for sesame breeder to understand genetic relationship of some agronomic characters and select promising cross lines for the development of new sesame variety.