• The Korean Journal of Applied Statistics
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• v.1 no.1
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• pp.75-90
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• 1987

Statistical data analysis using the statisticaal packages can be performed on the personal computers. But it is not easy to select a personal computer in which statisticians could run statistical packages. The paper discusses some of the minimum requirements of the personal computers to use statistical packages and how to choose good statistical packages with better numerical results and introduces the statistical packages which are available in the personal computers.

• Journal of Korean Society for Quality Management
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• v.13 no.1
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• pp.12-19
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• 1985

First of all, the necessity of statistical packages, and the strengths and weaknesses of microcomputers for statistical data ana!ysis are examined in this paper. Secondly, some statistical packages available for microcomputers in the international market are introduced, and the contents of two statistical packages developed by the author are presented.

• The Korean Journal of Applied Statistics
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• v.4 no.1
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• pp.85-92
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• 1991

This paper describes the trend and inherent problems of the current statistical packages, and statistical expert system is suggested as an alternative to the conventional statistical packages. The paper then describes the components and characteristics of statistical expert system, and suggests a strategy to choose development tools to build a system.

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.103-115
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• 2005

Mathematical packages have the advantages of symbolic computation and powerful graphics interface in contrast with statistical packages. We can use mathematical packages as a support tool in education of mathematics for statistics.

• The Korean Journal of Applied Statistics
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• v.10 no.1
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• pp.29-36
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• 1997

Four statistical packages useful for statistical process control are reviewed. MINITAB, RS, SAS, and SPSS are compared in terms of general capability, control chart, process capability, design of experiment and Taguchi method.

• Journal for History of Mathematics
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• v.11 no.1
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• pp.52-57
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• 1998

Since cluster analysis encompasses many diverse techniques for discovering structure within complex bodies of data, it has been employed as an effective tool in scientific inquiry. Recent works on cluster analysis softwares carried out by SAS, SPSS, S-PLUS and BMDP are briefly summarized and investigated in this paper. The inferred statistical package for windows executing a nay for data analysis in modern statistical techniques has several merits superior to other packages. Especially, S-PLUS can be designed and tried out much faster than other statistical packages. S-PLUS provides a graphic which is interactive, informative, flexible ways of looking at data. Also, if a statistical computation time is long and programs are complex, these can be shorten by providing interfaces to the UNIX systems (or C, Fortran).

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.279-288
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• 1994

Most of the statistical packages being used in Korea, such as SAS or SPSS, are imported from foreign countries. Since these package are written in English, it is not easy for Korean to learn the statistical packages. Also, most of the users except statistician use these expensive packages only to draw pictures and to make tables. We introduce a Korean statistical package which can be used easily for general public.

• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.487-496
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• 2008

Recently survival analysis becomes popular in a variety of fields so that a number of statistical packages are developed for analyzing the survival model. In this paper, several types of survival models are introduced and considered briefly. In addition, widely used three packages(SAS, SPSS, and STATA) for survival data are reviewed and their characteristics are investigated.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.315-323
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• 2019

Panel data sets have recently been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Often a dichotomous dependent variable occur in survival analysis, biomedical and epidemiological studies that is analyzed by a generalized linear mixed effects model (GLMM). The most common estimation method for the binary panel data may be the maximum likelihood (ML). Many statistical packages provide ML estimates; however, the estimates are computed from numerically approximated likelihood function. For instance, R packages, pglm (Croissant, 2017) approximate the likelihood function by the Gauss-Hermite quadratures, while Rchoice (Sarrias, Journal of Statistical Software, 74, 1-31, 2016) use a Monte Carlo integration method for the approximation. As a result, it can be observed that different packages give different results because of different numerical computation methods. In this note, we discuss the pros and cons of numerical methods compared with the exact computation method.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.437-447
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• 2009

Recently, in addition to analyzing data with appropriate statistical methods, statistical analysts in the industrial fields face difficulties that they have to compose proper datasets for analysis objectives via extracting or generating processes from diverse data storage devices. In this paper we survey and compare many state-of-the-art data access technologies adopted by several commonly used statistical packages. More understanding of these technologies will help to reduce the costs occurring when analyzing large size of datasets in especially data mining works, and so to allow more time in applying statistical analysis methods.