• 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.

• 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.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.87-102
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• 2024

Digital transformation refers to the process by which a company or organization changes or innovates its existing business model or sales activities using digital technology. This requires the use of various digital technologies - cloud computing, IoT, artificial intelligence, etc. - to strengthen competitiveness in the market, improve customer experience, and discover new businesses. In addition, in order to derive knowledge and insight about the market, customers, and production environment, it is necessary to select the right data, preprocess the data to an analyzable state, and establish the right process for systematic analysis suitable for the purpose. The usefulness of such digital data is determined by the importance of pre-processing and the correct application of exploratory data analysis (EDA), which is useful for information and hypothesis exploration and visualization of knowledge and insights. In this paper, we reexamine the philosophy and basic concepts of EDA and discuss key visualization information, information expression methods based on the grammar of graphics, and the ACCENT principle, which is the final visualization review standard, for effective visualization.

• The Journal of Bigdata
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• v.5 no.1
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• pp.41-54
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• 2020

With the recent expansion of Internet channels and the development of financial technology and information and communication technology, direct overseas purchases have expanded. Although direct overseas purchases dominate consumers in terms of price and scarcity by providing relatively low-priced products and products that are difficult to obtain in Korea, there is a higher chance of consumer dissatisfaction in terms of delivery, product, A/S and refund than domestic purchases. Therefore, this study analyzed consumer dissatisfaction caused by active overseas direct purchase and studied ways to improve problems with overseas direct purchase. As a research method, Several statistical data were collected from the Korea Consumer Agency(KCA), the Korea Customs Service(KCS) and the Korea International Trade Association(KITA) and analyzed using the Exploratory Data Analysis Technique (EDA). The analysis confirmed that consumers were not well aware of information about direct overseas purchases and that the type or degree of consumer complaints varied depending on the type of purchase. Therefore, this study suggests a direction for the revitalization of overseas direct purchases by using EDA to identify the overall status of overseas direct purchases and consumer dissatisfaction and to improve them.

• The Korean Journal of Applied Statistics
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• v.3 no.2
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• pp.17-25
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• 1990

We will compare several statistical package programs on aspects of exploratory data analysis(EDA). Specifically, PC versions of Minitab, NCSS, SAS, SPSS/PC+, Statgraphics, and SYSTAT are reviewed.

• The Journal of Bigdata
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• v.3 no.1
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• pp.1-12
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• 2018

Since the beginning of the first online shopping mall, BEST 100 is being provided as the core of all shopping mall websites. BEST 100 is greatly important because consumers can identify popular products at a glance. However, there are only studies using sales outcome indicators, and prior studies using BEST 100 are insignificant. Therefore, this study selected 11 online shopping malls and compared their main characteristics. As a research method, exploratory data analysis technique (EDA) was used by crawling the BEST 100 components of each shopping mall website, such as product name, price, and free shipping check. As a result, the total average price of 11 shopping malls was 72,891.41 won. Sales texts were classified into 8 categories by text mining. The most common category was the fashion part, but it is significant that the setting of the category analyzed the marketing text, not the product attribute. This study has implications for understanding the current online market flow and suggesting future directions by using EDA.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.487-489
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• 2021

Most of the women in Korea are experiencing changes in their status as it is very difficult to re-access to the labor market due to life events ranging from marriage to re-employment. Therefore, in this study, a study was conducted in consideration of qualifications as a factor that can help women with career breaks in employment. As a data analysis method, exploratory data analysis (EDA), a statistical technique to find out structural characteristics, was used. Using the data of vocational training courses for women with career interruptions in Korea, we studied how much the acquisition of a certificate is helpful for employment.

• Journal of the Korean Applied Science and Technology
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• v.39 no.6
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• pp.853-863
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• 2022

In order to revitalize start-ups in the beauty field, this study attempted to derive characteristic patterns of changes in demand and differences in emotions and meaning for 'beauty start-ups' by dividing the period by year from 2019 to 2021 based on exploratory data analysis (EDA). Most of the search terms related to the keyword "beauty start-up" showed more interest in institutions or certificates that can learn beauty skills than professional start-up education, which still does not recognize the importance of start-up education, and as an alternative, it is necessary to develop customized start-up education programs for each major. We establish hypotheses through exploratory data analysis and verify hypotheses by combining traditional corroborative data analysis (CDA). There has never been an exploratory data analysis method for beauty startups, and rather than mentioning the need for formal start-up education, analyzing changes in interest in beauty startups and the requirements of prospective start-ups with exploratory data will help develop customized start-up programs.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.7
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• pp.798-809
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• 2020

Evidence-based policymaking and assessments for scientific administration have increased the importance of statistics (data) utilization. Statistics can explain specific phenomena by providing numerical values and are a public resource for national decision making. Due to these inherent attributes, statistics are utilized as baseline and base data for government policy determinations and the analysis of various phenomena. However, compared to the importance, the role of statistics is limited, and statistics are often used as simple abstracts, produced mainly for suppliers, not for consumers' perspectives to create value. This study explores the statistical data and other attributes that can be utilized for policies or research to address the problems mentioned above. The baseline statistical data used in this study is from the Maritime Distress Accident Statistical Yearbook published by the South Korean Coast Guard, and other additional attributes are from text analyses of vessel casualty situation reports from the South Korean Maritime Police. Collecting 56 attributes drawn from the text analysis and executing an EDA resulted in 88 attribute unions: 18 attribute unions had a satisfactory significance probability (p-value < .05) and a strong correlation coefficient above 0.7, and 70 attribute unions had a middle correlation. (over 0.4 and under 0.7). Additionally, to utilize the extra attributes discovered from the EDA politically, a keyword analysis for each detailed strategy of the disaster Preparation basic plan was executed, the utilization availability of the attributes was obtained using a matching process of keywords, and the EDA deducted attributes were examined.

• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.185-194
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• 2010

In recommender systems which are used widely at e-commerce, collaborative filtering needs the information of user-ratings and neighbor user-ratings. These are an important value for recommendation in recommender systems. We investigate the in-formation of rating in NBCFA (neighbor Based Collaborative Filtering Algorithm), we suggest new algorithm that improve prediction accuracy of recommender system. After we analyze relations between two variable and Error Value (EV), we suggest new algorithm and apply it to fitted line. This fitted line uses Least Squares Method (LSM) in Exploratory Data Analysis (EDA). To compute the prediction value of new algorithm, the fitted line is applied to experimental data with fitted function. In order to confirm prediction accuracy of new algorithm, we applied new algorithm to increased sparsity data and total data. As a result of study, the prediction accuracy of recommender system in the new algorithm was more improved than current algorithm.