• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.11a
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• pp.3-4
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• 2008

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.05a
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• pp.687-688
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• 2012

• Education of Primary School Mathematics
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• v.11 no.1
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• pp.1-19
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• 2008

This study analyzed the repeated error patterns in division of fraction by elementary students through observation of their test papers. The questions for this study were following. First, what is the most changable thing among the repeated error patterns appeared in division of fraction by elementary students? Second, what is the most frequent error patterns in division of fraction by elementary students? First of all, the ratios of incorrect answers in division of fraction by general students were researched. This research was the only one time. The purpose was to know what kind of compositions in the problems were appeared more errors. Total 554 6th grade students(300 boys and 254 girls) from 6 elementary schools in Seoul are participated in this research. On the basis of this, the study for analysis began in earnest. 5 tests made progress for about 4 months. Total 181 6th grade students(92 boys and 89 girls) from S elementary school in Seoul were participated in this. After each test, to confirm the errors and to classify them were done. Then the repeated error patterns were arranged into 4 types: alpha, beta, gamma and delta type. Consequently, conclusions can be derived as follows. First, most students modify their errors as time goes by even though they make errors about already learned contents. Second, most students who appeared errors make them continually caused a reciprocal of natural number in the divisor when they calculate computations about '(fraction) $\div$ (natural number)'. Third, most students recognize that the divisor have to change the reciprocal when they calculate division of fraction through they modify their errors repeatedly.

• Journal of the Korean Statistical Society
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• v.36 no.2
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• pp.201-208
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• 2007

Evolutionary operation (EVOP) proposed by Box (1957) is a method for continuous monitoring and improvement of a full-scale manufacturing process with the objective of moving the operating conditions toward the better ones. EVOP consists of systematically making small changes in the levels of the two or three process variables under consideration. Data are collected on the response variable at each point of two level factorial design with the center point and a cycle is said to have been completed. The cycles are replicated sequentially until the decision is made on whether further cycle of experiments is needed to conclude the significance of any of main effects or interaction effects or the curvature. In this paper, an improved flow chart of EVOP is proposed and how to determine the number of cycles is studied based on the size of type II error. In order to reject the alternative hypothesis of interests with more confidence and conclude that we believe in the null hypothesis of no effects, we propose a counter measure $p^*-value$ corresponding to the p-value. The relationship of $p^*-value$ to the probability of type II error ${\beta}$ under the alternative hypothesis of interests is analogous to that of p-value to the probability of type I error ${\alpha}$. Also the implementation of EVOP with a mixture experiment is discussed.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.127-140
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• 2010

In interim analysis, group sequential tests are widely used for the ethical, scientific, and economic reasons. In this paper, we propose the group sequential tests using both type I and type II error spending rate functions when the response variable is discrete, especially binomial distribution, in the interim analysis. In addition, we propose new error spending rate function which covers the formerly proposed. Our method has good property that is flexible, fast and easily applicable. A numerical simulations are carried out to evaluate our method and it shows good performance.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.477-485
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• 2013

In this paper, we consider the sample sizes required for each group in independent two sample test of normal populations when both the type I error and type II error probabilities are specified with sample sizes and variances being possibly different. We derived the sample sizes and the power of the tests, and implement them by web programing. The result is available over the world wide web. Further, we also provide the power calculations and have them available on the web.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.347-349
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• 2014

Model verification and validation (V&V) is a current research topic to build computational models with high predictive capability by addressing the general concepts, processes and statistical techniques. The hypothesis test for validity check is one of the model validation techniques and gives a guideline to evaluate the validity of a computational model when limited experimental data only exist due to restricted test resources (e.g., time and budget). The hypothesis test for validity check mainly employ Type I error, the risk of rejecting the valid computational model, for the validity evaluation since quantification of Type II error is not feasible for model validation. However, Type II error, the risk of accepting invalid computational model, should be importantly considered for an engineered products having high risk on predicted results. This paper proposes a technique named as the response-adaptive experimental design to reduce Type II error by adaptively designing experimental conditions for the validation experiment. A tire tread block problem and a numerical example are employed to show the effectiveness of the response-adaptive experimental design for the validity evaluation.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.327-335
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• 2006

Case Based Reasoning (CBR) has come to be considered as an appropriate technique for diagnosis, prognosis and prescription in medicine. However, canventional CBR has a limitation in that it cannot incorporate asymmetric misclassification cast. It assumes that the cast of type1 error and type2 error are the same, so it cannot be modified according ta the error cast of each type. This problem provides major disincentive to apply conventional CBR ta many real world cases that have different casts associated with different types of error. Medical diagnosis is an important example. In this paper we suggest the new knowledge extraction technique called Cast-Sensitive Case Based Reasoning (CSCBR) that can incorporate unequal misclassification cast. The main idea involves a dynamic adaptation of the optimal classification boundary paint and the number of neighbors that minimize the tatol misclassification cast according ta the error casts. Our technique uses a genetic algorithm (GA) for finding these two feature vectors of CSCBR. We apply this new method ta diabetes datasets and compare the results with those of the cast-sensitive methods, C5.0 and CART. The results of this paper shaw that the proposed technique outperforms other methods and overcomes the limitation of conventional CBR.

• Journal of the Korea Safety Management & Science
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• v.10 no.2
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• pp.205-210
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• 2008

The research interprets the principles of sampling error design for quality statistics models such as hypothesis test, interval estimation, control charts and acceptance sampling. Introducing the proper discussions of the design of significance level according to the use of hypothesis test, then it presents two methods to interpret significance by Neyman-Pearson and Fisher. Second point of the study proposes the design of confidence level for interval estimation by Bayesian confidence set, frequentist confidential set and fiducial interval. Third, the content also indicates the design of type I error and type II error considering both productivity and customer claim for control chart. Finally, the study reflects the design of producer's risk with operating charistictics curve, screening and switch rules for the purpose of purchasing and subcontraction.

• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.445-453
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• 2011

Almost all small area estimations are obtained by minimizing the mean squared error. Recently relative error prediction methods have been developed and adapted to small area estimation. Usually the estimators obtained by using relative error prediction is called a shrinkage estimator. Especially when data set consists of large range values, the shrinkage estimator is known as having good statistical properties and an easy interpretation. In this paper we study the shrinkage estimators based on logistic regression type estimators for small area estimation. Some simulation studies are performed and the Economically Active Population Survey data of 2005 is used for comparison.