• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1103-1112
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• 2013

Exploratory data analysis focuses mostly on data exploration instead of model fitting. We can use the recurrence plot as a graphical exploratory data analysis tool. With the recurrence plot, we can obtain the structural pattern of the time series and recognize the structural change points in time series at a glance.

• Proceedings of the Safety Management and Science Conference
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• 2010.04a
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• pp.253-258
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• 2010

The paper reviews the methodologies of confirmatory data analysis(CDA) and exploratory data analysis(EDA) in statistical quality control(SQC), design of experiment(DOE) and reliability engineering(RE). The study discusses the properties of flexibility, openness, resistance and reexpression for EDA.