• IE interfaces
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• v.17 no.1
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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.

• Journal of Sasang Constitutional Medicine
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• v.28 no.1
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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.

• Journal of Veterinary Clinics
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• v.27 no.2
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• pp.170-174
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• 2010

With the ease availability of statistical software and powerful computers the application of statistical methods in domestic veterinary journals is on the increase. In parallel with this benefit, statistical errors are not uncommon even in renowned scientific and medical journals. These errors may lead to misinterpretation of the data, thereby, subjected to faulty conclusions. A systematic review of articles published in 8 issues of the Journal of Veterinary Clinics during 2006-2007 was performed to assess the statistical methodology and reporting. Ninety-four (72.9%) articles of the 129 original articles screened included any inferential statistical analysis in the article, including comparison of 3 or more groups (53 or 56.4%), comparison of independent 2 groups (40 or 42.6%), and paired t-test (9 or 9.6%) in order. Of the 94 articles in which statistical analysis was done 62 (or 66.0%) had at least 1 statistical error. Errors included failure to apply or incorrectly applying independent Student's t-test for paired data or vice versa, inappropriate use of t-test for more than 3 groups and failure in chi-square test to consider continuity-correction for small expected frequencies. The common errors in ANOVA were failure to validate assumption of the test, inappropriate post-hoc multiple-comparison and incorrect assumption of independence of data in repeated measures design. Reporting errors included failure to state statistical methods and failure to state specific test if more than 1 test was done. It is suggested that an editorial effort would be necessary to achieve the improvement of appropriate statistical procedures through the publication of statistical guidelines to author(s).

• Communications for Statistical Applications and Methods
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• v.1 no.1
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• pp.1-1
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• 1994

• Herbal Formula Science
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• v.17 no.1
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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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• v.21 no.4
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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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• v.27 no.3
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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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• v.19 no.4
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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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• v.3 no.3
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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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• v.53 no.5
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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.