• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1413-1421
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• 2016

In this paper graphical exploratory data analyses are proposed for ball games in sports. The plot of sequence of scoring points of each team can be used to see how the playing game has been processed until the end of each set or quarter. With the plot of sequential score differences through all the games we can see a dominance of each team and the times of score changes, i.e., turnovers. The ternary plots show the contours of scoring compositions for each player and enable us to compare the scoring patterns of each team if any. Using the score sequence plot we also can see the score pattern distribution of players. For demonstration we use the results of the gold medal match between Russia and Brazil for men's volleyball and between USA and Spain for men's basketball at the London 2012 Summer Olympics.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.295-300
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• 1998

Recently graphical modelling is being studied as a useful process analysis tool for exploratory causal analysis. Graphical modelling is a presentation method that uses graphs to describe statistical models of the structures of multivariate data. This paper describes an application of this graphical modeling with two cases from the automotive parts industry. One case is the unbalance problem of the pulley, an automotive generator part. There is multivariate data of the product from each of the processes which are connected in the series. By means of exploratory causal analysis between the variables using graphical modeling, the key processes which causes the variation of the final characteristics and their mechanism of the causal relationship have become clear. Another case is, also, the unbalanced problem of automotive starter parts which consists of many parts and is manufactured by complex machinery and assembling process. By means of the similar technique, the key processes are obtained easily and the results are reasonable from technical knowledge.

• The Korean Journal of Applied Statistics
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• v.29 no.2
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• pp.355-368
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• 2016

Outliers and influential data points distort many data analysis measures. Jang and Anderson-Cook (2014) proposed a graphical method called a rework plot for exploratory analysis purpose so that there could be a possible visualization of the trace of the impact of the possible outlying and/or influential data points on the univariate/bivariate data analysis and regression. They developed 3-D plot as well as pairwise plot for the appropriate measures of interest. This paper further extends their approach to identify its strength. We can use rework plots as a graphical exploratory data analysis tool to evaluate the impact of outliers in skewness and kurtosis of univariate data.

• 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.

• 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.

• 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.

• 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.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.227-237
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• 2014

We use the regression model with the indicator variables in the case that we use qualitative variables as some predictor variables in regression analysis. We use the ANCOVA(Analysis of Covariance) model when comparing the response variable among groups while statistically controlling for variation in the response variable caused by a variation in the covariate. DD-plot can be used as a graphical exploratory data analysis tool before the confirmatory data analysis. With the DD-plot, we can discriminate the difference of groups in the regression model with the indicator variables or the ANCOVA model at a glance. Making DD-plot does not demand the statistical model assumption about error terms in regression model. Several examples show the usefulness of DD-plots as a graphical exploratory data analysis tool for the regression analysis.