• The Korean Journal of Applied Statistics
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• v.6 no.1
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• pp.13-22
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• 1993

SAS/GRAPH is a part of the SAS System which generates information and presentation color graphics. It is able to import any SAS dataset from other statistical data analysis procedures and produce sophisticated graphics output. It also supports most output devices on the market and offers various tools enhancing graphics output. In this regard SAS/GRAPH outclasses its compertitors. However, it does not support an interactive tool for data visualization and graphical data analysis. As far as interactive statistical graphics is concerned, SAS/GRAPH is behind in features and functions, compared to newly emerged statsitical graphics software such as S-Plus.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.609-617
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• 2004

To present graph of probability densities, many softwares and graphical tools use methods that link points or straight lines. However, the methods can't display exactly and smoothly the graph and are not efficient from the viewpoint of process time. One method to overcome these shortcomings is utilizing interpolation methods. In these methods, selecting the number and location of knots is an important factor. This article proposes an algorithm to select knots for graphically presenting densities and implements graph components based on the algorithm.

• Journal of Elementary Mathematics Education in Korea
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• v.23 no.1
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• pp.119-141
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• 2019

This study was carried out in order to draw some implications for teaching statistical graph in mathematics education with respect to practical statistics education for promoting students' statistical literacy. We analyze 133 graphs appearing in 99 elementary textbooks of other subjects (subjects except from mathematics) by subjects and types, and identify some cases of graphs addressed by other subjects. As a results, statistical graph was most addressed in social studies, and bar graphs, line graphs, tables, and circle graphs are most used in other subjects. Moreover, there are some issues related to contents-(1) the problem of curricular sequencing between mathematics and other subjects, (2) the level of addressing ratio graph, and (3) the use of wavy lines. In terms of forms, (1) the visual variation of graphical representations, (2) representation combining multiple graphs, and (2) graphs specialized for particular subjects are drawn as other issues. We suggest some implications to be considered when teaching the statistical graph in elementary mathematics classes.

• Journal of Elementary Mathematics Education in Korea
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• v.24 no.1
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• pp.1-30
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• 2020

This study was carried out in order to identify the error types of statistical graphs for statistical literacy education. We analyze the meaning of using graphs in statistical problem solving, and identify categories, frequencies, and contexts as the components of statistical graphs. Error types of representing categories and frequencies make statistics consumers see incorrect distributions of data by subjective point of view of statistics producers and visual illusion. Error types of providing contexts hinder the interpretation of statistical information by concealing or twisting the contexts of data. Moreover, the findings show that tasks provide standardized frame already for drawing graphs in order to avoid errors and pay attention to the process of drawing the graph rather than statistical literacy for analyzing data. We suggest some implications about statistical literacy education, ethical problems, and knowledge for teaching to be considered when teaching the statistical graph in elementary mathematics classes.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.85-90
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• 2003

In this paper we develop a method for finding optimal ordering of K statistical models. This is based on a dependent paired comparison experimental arrangement whose results can naturally be represented by a completely oriented graph (also so called tournament graph). Introducing preference probabilities, strong transitivity conditions, and an optimal criterion to the graph, we show that a Hamiltonian path obtained from row sum ranking is the optimal ordering. Necessary theories involved in the method and computation are provided. As an application of the method, generalized variances of K multivariate normal populations are compared by a Bayesian approach.

• Research in Mathematical Education
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• v.10 no.4 s.28
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• pp.259-288
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• 2006

This study considers the performance of students in Grades 5 to 10 on four tasks assessing students' ability to evaluate data presented in numerical form, for example, in a list or table, or in graphical form, for example, in a frequency graph or scatter graph. The ability to tell a story from data or a graph is an important aspect of statistical literacy. The samples provide the opportunity to consider the association of two pairs of items, one from each type of interpretation, numerical and graphical. Educational implications for the outcomes and the classroom use of the items are considered.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.971-981
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• 2012

Google map has become one of the most recognized and comfortable means for providing statistical information of geographically referenced data. In this article, we introduce R functions to embed google map images on R interface and develop a function to represent statistical graphs such as bar graph, pie chart, and rectangle graph on a google map images.

• The Korean Journal of Applied Statistics
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• v.6 no.1
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• pp.1-11
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• 1993

Statistical graphics have been important new tools for data analysis and many statistical softwares are exploiting graphical methods. Among these softwares available in personal computer at low cost, we intriduce S-PLUS(version 2.0). S-PLUS is an interactive graphical data analysis system and object-oriented programming language. SAS/GRAPH is another popular graphical system for displaying data in the form of color plots, charts, maps, and slides on screen and hardcopy devices. S-PLUS is compared to SAS/GRAPH(version 6.04) in viewpoints of statistical graphics.

• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.569-578
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• 2020

Various graph clustering methods have been introduced to identify communities in social or biological networks. This paper studies the entropy-based and the Markov chain-based methods in clustering the undirected graph. We examine the performance of two clustering methods with conventional methods based on quality measures of clustering. For the real applications, we collect the mathematical subject classification (MSC) codes of research papers from published mathematical databases and construct the weighted code-to-document matrix for applying graph clustering methods. We pursue to group MSC codes into the same cluster if the corresponding MSC codes appear in many papers simultaneously. We compare the MSC clustering results based on the several assessment measures and conclude that the Markov chain-based method is suitable for clustering the MSC codes.

• Molecular & Cellular Toxicology
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• v.1 no.4
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• pp.281-283
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• 2005

Extraction of biologically meaningful data and their validation are very important for toxicogenomics study because it deals with huge amount of heterogeneous data. BINGO is an annotation mining tool for biological interpretation of gene groups. Several statistical modeling approaches using Gene Ontology (GO) have been employed in many programs for that purpose. The statistical methodologies are useful in investigating the most significant GO attributes in a gene group, but the coherence of the resultant GO attributes over the entire group is rarely assessed. BINGO complements the statistical methods with graph-theoretic measures using the GO directed acyclic graph (DAG) structure. In addition, BINGO visualizes the consistency of a gene group more intuitively with a group-based GO subgraph. The input group can be any interesting list of genes or gene products regardless of its generation process if the group is built under a functional congruency hypothesis such as gene clusters from DNA microarray analysis.