• Communications for Statistical Applications and Methods
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• 제7권1호
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• pp.313-313
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• 2000

• 한국한의학연구원논문집
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• 제18권2호
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• pp.55-64
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• 2012

Objectives: This study was performed to review the use of statistical analysis methods for the Traditional Korean Medicine studies listed on the Korea Citation Index from 1999 to 2008. Methods: A total of 4217 studies published on four journals of Traditional Korean Medicine were screened and 2682 articles using statistical methods were selected for the review. The selected studies were analysed according to their published year, statistical method and statistical package for use. Results: Statistical methods were used steadily in 64.6% of the articles after 2001, the most used statistical methods(57%) were mean difference comparison between 2 groups. The number of statistical methods mostly used in one article was identified as one in 1931 articles (72.0%). Duncan (36.8%) and Tukey (26.5%) were used for the ANOVA post hoc analysis. SPSS was most frequently used 68% out of Statistical package programs.(the number of mean difference comparison among more than 3 groups was continuously increasing and that makes post hoc being used. skills of statistical methods need to be diversified.) Conclusion: The interest on the proper use of statistical analysis in the research is increasing. This study will contribute to the Evidence-based Teaching on research methodology in Traditional Korean Medicine.

• Communications for Statistical Applications and Methods
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• 제11권1호
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• pp.59-77
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• 2004

Since the introduction of DNA microarray, a revolutionary high through-put biological technology, a lot of papers have been published to deal with the analyses of the gene expression data from the microarray. In this paper we review most papers relevant to the cDNA microarray data, classify them in statistical methods' point of view, and present some statistical methods deserving consideration and future study.

• Communications for Statistical Applications and Methods
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• 제14권2호
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• pp.377-388
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• 2007

As one of multi-class classification methods, ECOC (Error Correcting Output Coding) method is known to have low classification error rate. This paper aims at suggesting effective multi-class classification method (1) by comparing various encoding methods and decoding methods in ECOC method and (2) by comparing ECOC method and direct classification method. Both SVM (Support Vector Machine) and logistic regression model were used as binary classifiers in comparison.

• Management Science and Financial Engineering
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• 제13권1호
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• pp.1-24
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• 2007

I propose new combined randomized methods for global optimization problems. These methods are based on the Nested Partitions(NP) method, a useful method for simulation optimization which guarantees global optimal solution but has several shortcomings. To overcome these shortcomings I hired various statistical selection methods and combined with NP method. I first explain the NP method and statistical selection method. And after that I present a detail description of proposed new combined methods and show the results of an application. As well as, I show how these combined methods can be considered in case of computing budget limit problem.

• Asia pacific journal of information systems
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• 제7권2호
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• pp.77-102
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• 1997

The use of statistical methods in the MIS research is increasing. However, there has been a meager attempt to critically evaluate the use statistical methods in MIS research papers. The review of 33G papers published in MIS Quarterly from Volume 1 to 14 resulted in three findings; (1) the portion of empirical research has been gradually increasing compared with non-empirical research, (2) univariate parametric statistical methods are most popular among MIS researchers, (3) researchers do not comply with the writing code of scientific research. This paper disscusses what errors MIS researchers might commit in using statistical research methods and how to prevent those errors in each of three stages of research, research design phase, statistical inference phase, and interpretation phase.

• Communications for Statistical Applications and Methods
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• 제11권3호
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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.

• 대한간호학회지
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• 제30권6호
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• pp.1503-1520
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• 2000

The purpose of the present study is 1) to explain major effect models (main, moderating, and mediating) of social support and statistical methods for testing the effect models and 2) to analyze and evaluate the consistency in the use of the effect models and its statistical methods in Korean nursing studies. A total of 57 studies were selected from Journal of Korean Academy of Nursing, Journal of Korean Academic Society of Adult Nursing, Journal of Korean Women's Health Nursing Academic Society, Journal of Fundamentals of Nursing, Journal of Korean Community Nursing, Journal of Korean Psychiatric and Mental Health Nursing Academic Society, and Journal of Korean Pediatric Nursing Academic Society published in the year of 1990-1999. In results, most studies on social support performed in Korea Nursing Society were about a main effect model. There are few studies on moderating or mediating model of social support. Thus, it was difficult to find research findings how, why, under what conditions social support impacted on health outcomes. Most studies on the moderating or mediating effect model of social support used statistical methods for testing main effect model rather than for testing moderating or mediating effect model. That is, there are inconsistency between effect models of social support and its statistical methods in Korean nursing researches. Therefore, it is recommended to perform studies on moderating or mediating effect model and use appropriate statistical methods.

• The Korean Journal of Pain
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• 제23권1호
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• pp.35-41
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• 2010

Background: Statistical analysis is essential in regard to obtaining objective reliability for medical research. However, medical researchers do not have enough statistical knowledge to properly analyze their study data. To help understand and potentially alleviate this problem, we have analyzed the statistical methods and errors of articles published in the Korean Journal of Pain (KJP), with the intention to improve the statistical quality of the journal. Methods: All the articles, except case reports and editorials, published from 2004 to 2008 in the KJP were reviewed. The types of applied statistical methods and errors in the articles were evaluated. Results: One hundred and thirty-nine original articles were reviewed. Inferential statistics and descriptive statistics were used in 119 papers and 20 papers, respectively. Only 20.9% of the papers were free from statistical errors. The most commonly adopted statistical method was the t-test (21.0%) followed by the chi-square test (15.9%). Errors of omission were encountered 101 times in 70 papers. Among the errors of omission, "no statistics used even though statistical methods were required" was the most common (40.6%). The errors of commission were encountered 165 times in 86 papers, among which "parametric inference for nonparametric data" was the most common (33.9%). Conclusions: We found various types of statistical errors in the articles published in the KJP. This suggests that meticulous attention should be given not only in the applying statistical procedures but also in the reviewing process to improve the value of the article.

• 한국산학기술학회논문지
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• 제17권5호
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• pp.645-652
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• 2016

입력 랜덤 변수(input random variable)의 통계 모델링은 기계시스템의 신뢰성 해석(reliability analysis), 신뢰성 기반 설계(reliability-based design optimization), 해석모델의 통계적 검정(validation) 및 보정(calibration)을 위해 반드시 필요하다. 대표적인 통계모델링 기법에는 Akaike Information Criterion (AIC), AIC correction (AICc), Bayesian Information Criterion, Maximum Likelihood Estimation (MLE), Bayesian 방법 등이 있다. 이러한 방법들은 기본적으로 주어진 데이터로부터 후보 모델의 우도함수값을 이용하여 후보 모델 중 가장 적합한 모델을 선택하는 방법이며, 방법에 따라 데이터 수 혹은 파라미터의 수를 고려하여 모델을 선정한다. 하지만 실제 현장에서 데이터의 통계모델링을 하는 엔지니어는 각 방법의 장단점에 대한 이해가 부족하여 어떤 방법이 정확한 방법인지 몰라 통계모델링 수행 시 어려움이 있다. 본 논문에서는 다양한 통계모델링 방법들을 비교하고 각 방법의 장단점 분석을 통해 가장 적합한 모델링 기법을 제안하고자 한다. 각 방법의 검증을 위해 다양한 모분포를 가정하고 다양한 사이즈의 샘플을 임의로 생성하여 시뮬레이션을 수행하였으며, 실제 공학 데이터를 사용하여 통계모델링 방법의 유효성을 검증하였다.