• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.257-266
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• 2012

R is a free software for statistical analysis that provides simple interfaces to other application programs. Many people are trying to learn R, but it is difficult to learn R compared to commercial software such as SPSS or SAS, and it is cumbersome to provide an environment to teach R. Thus, it is essential to provide a new web-based R environment for novice users or for laboratory use. We developedWebER (a web-based R environment) using PHP on the Linux apache server. WebER can be easily used by any R user because we implemented the same functions as the basic Rgui such as editing R program, generating the text, image outputs, errors and warnings. It is also possible for multi-users to access WebER.

• Journal of Korean Society for Quality Management
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• v.27 no.2
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• pp.277-290
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• 1999

In this paper we compare several statistical packages and propose basic requirements of the package for the efficient education of statistics. We develop a statistical software, KESS, based on Excel. KESS provides all the input menus and output results in Korean.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.797-801
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• 2003

The Statistical Energy Analysis is based on the power flow and the energy conservation between sub-systems, which enable the prediction of acoustic and structural vibration behavior in mid-high frequency ranges. This paper discusses the identification of SEA coupling loss factor parameters from experimental measurements of small reverberation chamber sound pressure levels and structural accelerations. As structural subsystems, steel plates with and without damping treatment are considered. Calculated CLFs were verified by both transmission loss values for air-borne CLF case and running SEA commercial software As a result, CLFs have shown a good agreement with those computed by software. Acoustical behavior of air-borne noise and structure-borne noise has been examined. which shows reasonable results, too.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.102-109
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• 2021

Since the 4th Industrial Revolution is implemented based on superintelligence, new insights must be provided through convergence studies with other fields to find optimal solutions to create new ideas. In this paper, we intende to present improvement measures for probability and statistical education, which is an athlete's subject on data analysis and processing in the ICT(Information & Communication Technologies) field in the era of superintelligence of the 4th industrial revolution. This paper aims to strengthen competitiveness through early development and commercialization of new technologies by presenting probabilities and statistical curriculums that require linkage in the ICT field. Second, it is necessary to present an educational system diagram linking probabilities and statistics in the ICT field to prepare a mid- to long-term response strategy for ICT education in response to innovative changes. Third, through a survey, we intend to present an effective educational operation plan linking probability and statistics to ICT major subjects by analyzing the perception of probability, statistical importance, and utilization of majors in this field.

• The Korean Journal of Applied Statistics
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• v.33 no.1
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• pp.87-105
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• 2020

Statistical engineering contains the design of experiments, quality control/management, and reliability engineering. R is a free software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. R package has many functions and libraries for statistical engineering. We can use R package as a useful tool for statistical engineering. This paper shows the applications of R package for statistical engineering and suggests a R Task View for statistical engineering.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.741-750
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• 2011

In this paper, we derive an software mixed debugging model based on a Markov process, assuming that the length of time to perform the debugging is random and its distribution may depend on the fault type causing the failure. We assume that the debugging process starts as soon as a software failure occurs, and either a perfect debugging or an imperfect debugging is performed upon each fault type. One type is caused by a fault that is easily corrected and in this case, the perfect debugging process is performed. An Imperfect debugging process is performed to fix the failure caused by a fault that is difficult to correct. Distribution of the first passage time and working probability of the software system are obtained; in addition, an availability function of a software system which is the probability that the software is in working at a given time, is derived. Numerical examples are provided for illustrative purposes.

• Asia pacific journal of information systems
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• v.30 no.3
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• pp.457-496
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• 2020

This study aims to extensively analyze the performance of various Machine Learning (ML) techniques for predicting version to version change-proneness of source code Java files. 17 object-oriented metrics have been utilized in this work for predicting change-prone files using 31 ML techniques and the framework proposed has been implemented on various consecutive releases of two Java-based software projects available as plug-ins. 10-fold and inter-release validation methods have been employed to validate the models and statistical tests provide supplementary information regarding the reliability and significance of the results. The results of experiments conducted in this article indicate that the ML techniques perform differently under the different validation settings. The results also confirm the proficiency of the selected ML techniques in lieu of developing change-proneness prediction models which could aid the software engineers in the initial stages of software development for classifying change-prone Java files of a software, in turn aiding in the trend estimation of change-proneness over future versions.

• Journal of the Korean Statistical Society
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• v.30 no.2
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• pp.219-230
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• 2001

How do you carry out data analysis\ulcorner There are few texts and little theory. One approach could be to use a pattern language, an idea which has been successful in field as diverse as town planning and software engineering. Patterns for data analysis are defined and discussed, illustrated with examples.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.69-76
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• 1999

A new statistic {{{{ {d }`_{2 } ^{s } }}}} is introduced for constructing co ntrol limits. It is easier and more convienient than d2 We will show the characteristic of {{{{ {d }`_{2 } ^{s } }}}} and evaluate {{{{ {d }`_{2 } ^{s } }}}} through average run length(ARL).

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.633-658
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• 2021

Statistical engineering contains design of experiments, quality control/ management, and reliability engineering. Python is a free software environment for machine learning, data science, and graphics. Python package has many functions and libraries for statistical engineering. We can use Python package as a useful tool for statistical engineering. This paper shows applications of Python package for statistical engineering and suggests a total Python projects for statistical engineering.