• 산업공학
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• 제17권1호
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• pp.1-12
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• 2004

In a modern semiconductor device manufacturing industry, statistical bin limits on wafer level test bin data are used for minimizing value added to defective product as well as protecting end customers from potential quality and reliability excursion. Most wafer level test bin data show skewed distributions. By Monte Carlo simulation, this paper evaluates methods and sample size effect regarding determination of statistical bin limits. In the simulation, it is assumed that wafer level test bin data follow the Poisson distribution. Hence, typical shapes of the data distribution can be specified in terms of the distribution's parameter. This study examines three different methods; 1) percentile based methodology; 2) data transformation; and 3) Poisson model fitting. The mean square error is adopted as a performance measure for each simulation scenario. Then, a case study is presented. Results show that the percentile and transformation based methods give more stable statistical bin limits associated with the real dataset. However, with highly skewed distributions, the transformation based method should be used with caution in determining statistical bin limits. When the data are well fitted to a certain probability distribution, the model fitting approach can be used in the determination. As for the sample size effect, the mean square error seems to reduce exponentially according to the sample size.

• 사상체질의학회지
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• 제28권1호
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• pp.51-56
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• 2016

Objectives This study was to identify the types of errors in the statistical analysis and trends of previous reported papers that used various statistical techniques.Methods We have selected 118 original articles for statistical review from the OASIS(http://oasis.kiom.re.kr) and the Pubmed(http://www.pubmed.gov) in the field of Sasang constitutional medicine. Published year was restricted from 2011 to 2015.Results 1. The ANOVA(25.72%) was the statistic of choice overall, followed by the chi-square test(21.74%), regression analysis(14.13%), t-test(11.59%), and etc. 2. By examining the errors of the statistical methods, there were 42(59.2%) thesis with errors among 71 thesis using ANOVA, 19(31.7%) thesis among 60 thesis using chi-square test, and 35(89.7%) over 39 thesis using regression analysis.Conclusions To improve the quality of Sasang Constitution, the participation of statisticians in research design will reduce the significant errors in statistical interpretation of the results.

• 한국임상수의학회지
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• 제27권2호
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• pp.170-174
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• 2010

본 연구는 2006-2007년 한국임상수의학회지에 발표된 논문을 대상으로 자료 분석과 보고방법의 오류를 중심으로 검토하였다. 총 129편 중 94편이 적어도 한가지 이상의 통계분석을 수행하였으며, 분석기법으로는 세 집단 이상 비교 (53편, 56.4%), 두 독립표본 검정 (40편, 42.6%), 짝지은 표본 검정 (9편, 9.6%) 순으로 나타났다. 94편 중 62편 (66%)의 논문에서 적어도 한가지 이상의 통계적 오류가 발견되었다. 주요 오류로는 짝지은 표본에 대한 독립표본 검정, 세 집단 이상에 대한 t 검정의 반복, 카이제곱 검정에서 연속성 보정 무시, 분산분석에서 정규성 검토와 다중비교 방법 선택의 오류, 반복측정 자료에 대한 의존성 가정 무시, 통계분석 방법에 대한 부적절한 설명, 적용한 분석기법에 대한 구체적인 설명 부재 등으로 나타났다. 이러한 문제점을 개선하기 위해서는 학회차원에서 통계처리와 기술방법에 대한 가이드라인을 시급히 마련할 필요가 있을 것으로 사료된다.

• Communications for Statistical Applications and Methods
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• 제1권1호
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• pp.1-1
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• 1994

• 대한한의학방제학회지
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• 제17권1호
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• pp.75-83
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• 2009

Objective : The purpose of this study is to indicate of problems in statistical analysis method of "The Korean Journal of oriental Medical Prescription" and we will be proposed the useful application of the statistical analysis method. Methods : In this paper, we were analysed statistical analysis methodology from published journal articles "The Korean Journal of Oriental Medical Prescription" December, year 2000 to December, year 2008. We were investigated of problems in application of structured analysis methods those journal articles that including statistical analysis techniques and analysis methods. Results : 1. A random allocation of the experimental group and control groups are important factors in the planning process of statistical analysis. However, there are less explanation those journal articles. 2. There are no consideration in specimen size that there will be considerate by the level of significance and statistical test. 3. Many article authors were confused between parametric methods and non-parametric methods that they were applied parametric statistical analysis methods although inapplicable sample size. 4. There were applied the parametric methods consists of t-test instead non-parametric methods in the comparison of average intergroup relations. 5. There were less understanding posterior analysis and were confused with t-test. Conclusion : Our goal was to outline the key methods with a brief discussion of problems(statistical analysis methods), avenues for solutions. we recommend authors to use an appropriate statistical analysis methods for obtaining a more cautions results.

• Communications for Statistical Applications and Methods
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• 제21권4호
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• pp.309-316
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• 2014

Testing normality is very important because the most common assumption is normality in statistical analysis. We propose a new plot and test statistic to goodness-of-fit test for normality based on the generalized Lorenz curve. We compare the new plot with the Q-Q plot. We also compare the new test statistic with the Kolmogorov-Smirnov (KS), Cramer-von Mises (CVM), Anderson-Darling (AD), Shapiro-Francia (SF), and Shapiro-Wilks (W) test statistic in terms of the power of the test through by Monte Carlo method. As a result, new plot is clearly classified normality and non-normality than Q-Q plot; in addition, the new test statistic is more powerful than the other test statistics for asymmetrical distribution. We check the proposed test statistic and plot using Hodgkin's disease data.

• Communications for Statistical Applications and Methods
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• 제27권3호
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• pp.285-299
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• 2020

In many cases, we are interested in identifying independence between variables. For continuous random variables, correlation coefficients are often used to describe the relationship between variables; however, correlation does not imply independence. For finite discrete random variables, we can use the Pearson chi-square test to find independency. For the mixed type of continuous and discrete random variables, we do not have a general type of independent test. In this study, we develop a independence test of a continuous random variable and a discrete random variable without assuming a specific distribution using kernel density estimation. We provide some statistical criteria to test independence under some special settings and apply the proposed independence test to Pima Indian diabetes data. Through simulations, we calculate false positive rates and true positive rates to compare the proposed test and Kolmogorov-Smirnov test.

• Communications for Statistical Applications and Methods
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• 제19권4호
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• pp.607-617
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• 2012

The goodness-of-fit test for multivariate normal distribution is important because most multivariate statistical methods are based on the assumption of multivariate normality. We propose goodness-of-fit test statistics for multivariate normality based on the modified squared distance. The empirical percentage points of the null distribution of the proposed statistics are presented via numerical simulations. We compare performance of several test statistics through a Monte Carlo simulation.

• Advances in concrete construction
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• 제3권3호
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• pp.161-185
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• 2015

FRP confining is a widely used method for seismic retrofitting of concrete columns. Several studies investigated the stress-strain behavior of FRP confined concrete prisms with square and rectangular sections both experimentally and analytically. In some studies, the monotonic stress-strain behavior of confined concrete was investigated and compressive strength models were developed. To study the reliability of these models, thorough statistical tests are required. This paper aims to investigate the reliability of the presented models using statistical tests including t-test, wilcoxon rank sum test, wilcoxon signed rank test and sign test with a level of significance of 5%. Wilk Shapiro test was also employed to evaluate the normality of the data distribution. The results were compared for different cross section and confinement types. To see the accuracy of the models when there were no significant differences between the results, the coefficient of confidence was used.

• Nuclear Engineering and Technology
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• 제53권5호
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• pp.1664-1675
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• 2021

In order to verify the statistical performance of the nonparametric tests used in the MARSSIM approach, all plausible contamination distribution types that can be encountered in a survey area should be investigated. As the first of such investigations, this study aims to perform the verification for normal distribution of the contamination in a survey area by simulating the collection of random samples from it through the Monte Carlo simulation. The results of the simulations conducted for a total of 81 simulation cases showed that Sign test and WRS test both exhibited an excellent statistical performance: 100% for the former and 98.8% for the latter. Therefore, in final status surveys of the MARSSIM approach, a high statistical performance can be expected in applying the nonparametric hypothesis tests to survey areas whose net contamination can be assumed to be normally distributed.