• The Korean Journal of Applied Statistics
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• v.15 no.2
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• pp.433-448
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• 2002

Revelation is emphasized in exploratory data analysis. Hence, many graphical techniques are used in exploratory data analysis. Three applications of graphs in exploratory data analysis are presented.

• Journal of The Korean Association For Science Education
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• v.17 no.4
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• pp.481-492
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• 1997

The purpose of this study is to construct validity of Tripartite model of "Attitudes towards Science" using Exploratory and Confirmatory Factor Analyses. Exploratory and confirmatory factor analyses are two major approaches to factor analysis. The primary goal of factor analysis is to explain the covariances or correlations between many observed variables by means of relatively few underlying latent variables. In exploratory factor analysis, the number of latent variables is not determined before the analysis, all latent variables typically influence all observed variables, the measurement errors(${\delta}$) are not allowed to correlate, and unidentification of parameters is common. Confirmatory factor analysis requires a detailed and identified initial model. Confirmatory factor analysis techniques allow relations between latent and observed variables that are not possible with traditional, exploratory factor analysis techniques. As a result of exploratory factor analysis, tripartite model of "Attitudes towards Science" being composed of affection, behavioral intention and cognition is empirically identified. But attitude of science career being composed of affection and behavioral intention is identified. In validity test using confirmatory factor analysis, measurement structure of Tripartite model of "Attitudes towards Science" is not correspondent to data set. Because it is concluded that the object of attitudes are not specific.

• Proceedings of the Korean Statistical Society Conference
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• 2005.11a
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• pp.37-41
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• 2005

Exploratory data analysis(EDA) is the initial stage of data analysis and provides a useful overview about the whole microarray experiment. If the experiments are replicated, the analyst should check the quality and reliability of microarray data within same experimental condition before the deeper statistical analysis. We shows EDA method focusing on the quality and reproducibility for replicates.

• Knowledge Management Research
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• v.10 no.1
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• pp.53-70
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• 2009

This study empirically explored what promotes exploration, through a case analysis of a Korean SME (small and medium sized enterprise), based on the research framework which focuses on the identification and the selection of exploratory NPD (new product development) alternatives, and the accumulation of novel capabilities in new technology domains. The learning process of the exploratory NPD project described is as follows. The identification barrier of exploratory NPD project is relatively low. Constructive crisis is germane to selecting exploratory NPD alternatives and to enduring the long payback period. New separated R&D unit is likely to implement the exploratory NPD project. The length of the gestation period of the exploratory NPD project is related with the level of the conflict between old members and new members. This study identified several antecedents of the exploratory NPD project. Prior success promotes the identification process of the exploratory NPD projects. Constructive crisis is related with CEO's personal characteristics such as future oriented and proactive personality. The proactive involvement and persuasion of CEO are germane to reducing the conflict between old and new members and to the success of the exploratory NPD project. Based on the results, this study discusses several implications and future research directions.

• The Korean Journal of Emergency Medical Services
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• v.26 no.3
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• pp.7-19
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• 2022

Purpose: The purpose of this study was to confirm the exploratory factor analysis and reliability analysis of the burden of 119 emergency medical technicians. Methods: The data collection period was from November 2, 2022 to November 6, 2022. This study had 316 subjects, and the collected data were analyzed using exploratory factor analysis and Cronbach's α coefficient using IBM SPSS statistics 27.0. Results: The reliability was .924. The exploratory factor analysis yielded the following information: the first factor was lack of violence policy, the second factor was conflict between the organization and the paramedics, the third factor was lack of psychological support, and the fourth factor was lack of education and communication. The explanation power of 4 factors was 54.31%. Conclusion: This study is significant as it performs exploratory factor analysis as a preliminary step in the development of a burden measurement tool.

• Journal of Korean Society for Quality Management
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• v.49 no.1
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• pp.81-95
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• 2021

Purpose: The purpose of this study was to analysis pattern of nonconforming farmers who is one of the factors of unconformity in residual pesticides. Methods: Pattern analysis of nonconforming farmers were analyzed through convergence of safety data and farmer's DB data. Exploratory data analysis and association rule analysis were used for extracting factors related to unconformity. Results: The results of this study are as follows; regarding the exploratory data analysis, it was found that factors of farmers influencing unconformity in residual pesticides by total 9 factors; sampling time, gender, age, cultivation region, farming career, agricultural start form, type of agriculture, cultivation area, classification of agricultural products. Regarding the association rule analysis, non-conformity association rules were found over the past three years. There was a difference in the pattern of nonconforming farmers depending on the cultivation period. Conclusion: Exploratory data analysis and association rule analysis will be useful tools to establish more efficient and economical safety management plan for agricultural products.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.735-744
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• 2005

The number of electronic mail(E-mail) has been increased dramatically as a result of expanding internet and information technology. Although there are many conveniences of E-mail in the bright side, some serious problems occur because of E-mail in its dark side. One of the problems is spam-mail which is unsolicited mail and also called bulk mail. This paper presents a set of patterns of spam-mail occurrences within a week using the correspondence analysis. The correspondence analysis is an exploratory multivariate technique that converts data into a particular type of graphical display in which the rows and columns are depicted as points. One of the meaningful patterns is a great increment of adult and phishing related spam-mails at weekends so any spam-mail filters should be designed to cope with this pattern.

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.807-819
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• 2013

A recurrence plot can be used as a graphical exploratory data analysis tool before confirmatory time series analysis. With the recurrence plot, we can obtain the structural pattern of the time series and recognize the structural change points in a time series at a glance. Korean stock data shows the usefulness of the recurrence plot as a graphical exploratory data analysis tool for time series data.

• Journal of the Korean School Mathematics Society
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• v.1 no.1
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• pp.35-45
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• 1998

This paper will present some basic and simple graphical methods of exploratory data analysis for the instrument of data analysis at school mathematics. Human beings perceive visual patterns more readily than patterns in collections of numbers. This is especially important in exploratory data analysis because pictures dramatically reveal things that we did not expect to find in the data set. Here are graphical methods as the stem and leaf plot, the box plot, the star plot and the face plot. These methods impulse the motivation of students in real life. And the subject can be taught in secondary school with several applications. Also It is important for students to get a feel for working with and manipulating data before studying the more theoretical aspects of statistics.

• Genomics & Informatics
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• v.5 no.3
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• pp.129-132
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• 2007

arraylmpute is a software for exploratory analysis of missing data and imputation of missing values in microarray data. It also provides a comparative analysis of the imputed values obtained from various imputation methods. Thus, it allows the users to choose an appropriate imputation method for microarray data. It is built on R and provides a user-friendly graphical interface. Therefore, the users can easily use arraylmpute to explore, estimate missing data, and compare imputation methods for further analysis.