• Korean Journal of Acupuncture
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• v.29 no.4
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• pp.573-580
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• 2012

Objectives : This study was to investigate statistical validities and trends of previously reported papers that used various statistical techniques such as t-test and analysis of variance. Methods : To analyze the statistical procedures, 54 original articles using those statistical methods were selected from The Korean Journal of Acupuncture published from 2007 to 2011. Results : T-test and analysis of variance were used in 23(25.27%), and 18 papers(19.78%) out of 54 papers, respectively. Seven articles(12.96%) did not report alpha values and 26(48.15%) out of 54 studies were not tested for normal distribution. One paper(1.85%) misused t-test and 7 papers(38.89%) did not carry out the multiple comparison. Conclusions : To improve the quality of KJA, statistician involvement in research design would be necessary to reduce errors in statistical methods and interpretation of the results.

• Journal of the Korean Statistical Society
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• v.30 no.2
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• pp.179-192
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• 2001

When a statistical model has a hierarchical structure such as multilayer perceptrons in neural networks or Gaussian mixture density representation, the model includes distribution with unidentifiable parameters when the structure becomes redundant. Since the exact structure is unknown, we need to carry out statistical estimation or learning of parameters in such a model. From the geometrical point of view, distributions specified by unidentifiable parameters become a singular point in the parameter space. The problem has been remarked in many statistical models, and strange behaviors of the likelihood ratio statistics, when the null hypothesis is at a singular point, have been analyzed so far. The present paper studies asymptotic behaviors of the maximum likelihood estimator and the Bayesian predictive estimator, by using a simple cone model, and show that they are completely different from regular statistical models where the Cramer-Rao paradigm holds. At singularities, the Fisher information metric degenerates, implying that the cramer-Rao paradigm does no more hold, and that he classical model selection theory such as AIC and MDL cannot be applied. This paper is a first step to establish a new theory for analyzing the accuracy of estimation or learning at around singularities.

• Journal of Chest Surgery
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• v.27 no.9
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• pp.732-737
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• 1994

From January 1983 to December 1992, There were 1441 papers in the Korean Journal of Thoracic and Cardiovascular Surgery. Among these papers, 783[54.3%] were original article or clinical analysis and 652[45.2%] were case reports. A total of 319 papers contained some statistical analysis. In 150 cases[47.0%] of these 319 papers, the statistical description was insufficient. Of the correctly described papers, 115[68%] had more than one statistical error. Of course, in many cases the errors were not considered to be severe, but they were often sufficient to raise doubts about some inferences. We suggest that authors should be more careful when they describe and apply statistical methods. If possible, authors should interpret results with statistical specialists. And we also suggest that our society have more extensive statistical refereeing system. This would at least prevent the worst errors from appearing in print. The last suggestion is elementary instruction in statistical methods during preclinical training.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.437-447
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• 2009

Recently, in addition to analyzing data with appropriate statistical methods, statistical analysts in the industrial fields face difficulties that they have to compose proper datasets for analysis objectives via extracting or generating processes from diverse data storage devices. In this paper we survey and compare many state-of-the-art data access technologies adopted by several commonly used statistical packages. More understanding of these technologies will help to reduce the costs occurring when analyzing large size of datasets in especially data mining works, and so to allow more time in applying statistical analysis methods.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.421-429
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• 2013

Modern statistics is used in many fields; however many users face difficulties in understanding statistical concepts. On the other hand, elementary school curriculum covers stem and leaf plot, pie chart, charts for proportional data as well as descriptive statistics including the mean. We find that an "intuitive" statistical package focused on 5-8 graders for statistical education will help future statistics users understand statistical concepts at earlier stages of their lives.

• Education of Primary School Mathematics
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• v.12 no.2
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• pp.133-143
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• 2009

In this thesis, we conduct a comprehensive analysis of the current situation and the inherent problems found in modern statistics education in Elementary School. There are statistical curriculum, 7th textbook of elementary school level, practise of statistics class, connection of real life etc. Through analysis of these given problem, we explore the future direction of statistical education. Therefore, the statistical learning to make statistical situations and pose problems based on students' interests and students-related situations should be an effects on positive mathematical attitude and statistical thinking which could develop understanding statistical problems and thinking.

• The Mathematical Education
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• v.50 no.2
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• pp.185-212
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• 2011

The purpose of this study is to analyze whether spreadsheet-used instruction can improve statistical thinking ability and attitude and also to identify what characteristics of statistical thinking is constructed. For this study, a subject of 2 classes were randomly selected among the 12 classes of the 11th grader in D high school and designated one class as the experimental group and the other class as the control group. Eight hours of the spread sheet-used instruction and the traditional textbook-oriented instruction had been carried out in each class. The research findings are as follows. First, the spread sheet-used instruction is shown to be more effective in enhancing statistical thinking than the traditional textbook-oriented instruction. Second, the spread sheet-used instruction is shown to be more effective in improving statistical attitude than the traditional textbook-oriented instruction. Third, students have shown the various characteristics of statistical thinking in the data descriptive process, data arrange-summary process, data representing process, and data analying process through the spread sheet-used instructions. Hence, the spread sheet-used instruction is recommended in teaching statistics.

• Journal of Information Science Theory and Practice
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• v.11 no.2
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• pp.67-81
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• 2023

This paper presents a dynamic development of an ontology-based statistical information system supporting the collection, storage, processing, analysis, and the presentation of statistical knowledge at the national scale. To accomplish this, we propose a data mining technique to dynamically collect data relating to citizens from publicly available data sources; the collected data will then be structured, classified, categorized, and integrated into an ontology. Moreover, an intelligent platform is proposed in order to generate quantitative and qualitative statistical information based on the knowledge stored in the ontology. The main aims of our proposed system are to digitize administrative tasks and to provide reliable statistical information to governmental, economic, and social actors. The authorities will use the ontology-based statistical information system for strategic decision-making as it easily collects, produces, analyzes, and provides both quantitative and qualitative knowledge that will help to improve the administration and management of national political, social, and economic life.

• Smart Media Journal
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• v.3 no.3
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• pp.26-35
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• 2014

The statistical graphics is a design field focusing on the user perception aspects for the correct information delivery and the effective understanding, with the use of the quantitative data through the information analysis, extraction, visualization process. The statistical graphics with the big data composition factor is termed as the statistical big data graphics. In the statistical graphics the visual factors are used to reduce the errors in the perception part and to successfully deliver the information. However, in the statistical big data graphics the visual factors of the enormous data are causing the cognitive discouragements. The purpose of this study is to extract the cognitive discouragement factors from the big data statistical graphics, categorizing the types of the statistical big data graphics as 'network type', 'segment type', and 'mixed type', based on their compositional shapes, and explored the characteristics according to them. Especially, based on the visual main factors in the statistical big data graphics, We extracted the cognitive discouragement factors that appear in the high visualization as the four categories: 'multi-dimensional cases', 'various color', 'information overlap', and 'legibility of the writing'.

• Journal of The Korean Astronomical Society
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• v.40 no.4
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• pp.99-106
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• 2007

Statistical analyses were performed to investigate the relative success and accuracy of daily maximum X-ray flux (MXF) predictions, using both multilinear regression and autoregressive time-series prediction methods. As input data for this work, we used 14 solar activity parameters recorded over the prior 2 year period (1989-1990) during the solar maximum of cycle 22. We applied the multilinear regression method to the following three groups: all 14 variables (G1), the 2 so-called 'cause' variables (sunspot complexity and sunspot group area) showing the highest correlations with MXF (G2), and the 2 'effect' variables (previous day MXF and the number of flares stronger than C4 class) showing the highest correlations with MXF (G3). For the advanced three days forecast, we applied the autoregressive timeseries method to the MXF data (GT). We compared the statistical results of these groups for 1991 data, using several statistical measures obtained from a $2{\times}2$ contingency table for forecasted versus observed events. As a result, we found that the statistical results of G1 and G3 are nearly the same each other and the 'effect' variables (G3) are more reliable predictors than the 'cause' variables. It is also found that while the statistical results of GT are a little worse than those of G1 for relatively weak flares, they are comparable to each other for strong flares. In general, all statistical measures show good predictions from all groups, provided that the flares are weaker than about M5 class; stronger flares rapidly become difficult to predict well, which is probably due to statistical inaccuracies arising from their rarity. Our statistical results of all flares except for the X-class flares were confirmed by Yates' $X^2$ statistical significance tests, at the 99% confidence level. Based on our model testing, we recommend a practical strategy for solar X-ray flare predictions.