• Journal of applied mathematics & informatics
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• v.13 no.1_2
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• pp.479-485
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• 2003

The influence of observations on the Wilks' lambda test of a linear hypothesis in multivariate regression is investigated using the local influence method. The perturbation scheme of case-weights is considered. A numerical example is given to show the effectiveness of the local influence method in identifying the influential observations.

• Journal of the Korea Safety Management & Science
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• v.15 no.1
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• pp.283-292
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• 2013

This paper aims to propose a new steps of hypothesis testing using analysis process and improvement process in the six sigma DMAIC. The six sigma implementation models proposed in this paper consist of six steps. The first step is to establish a research hypothesis by specification directionality and FBP(Falsibility By Popper). The second step is to translate the research hypothesis such as RHAT(Research Hypothesis Absent Type) and RHPT(Research Hypothesis Present Type) into statistical hypothesis such as $H_0$(Null Hypothesis) and $H_1$(Alternative Hypothesis). The third step is to implement statistical hypothesis testing by PBC(Proof By Contradiction) and proper sample size. The fourth step is to interpret the result of statistical hypothesis test. The fifth step is to establish the best conditions of product and process conditions by experimental optimization and interval estimation. The sixth step is to draw a conclusion by considering practical significance and statistical significance. Important for both quality practitioners and academicians, case analysis on six sigma projects with implementation guidelines are provided.

• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.495-506
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• 1998

The proposed class of generalized logistic models, indexed by an extra parameter, can be used to model or to examine symmetric or asymmetric discrepancies from the logistic model. When there are a finite number of different design points, we are mainly concerned with maximum likelihood estimation of parameters and in deriving their large sample behavior A score test and a bootstrap hypothesis test are also considered to check if the standard logistic model is appropriate to fit the data or if a generalization is needed .

• International Journal of Reliability and Applications
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• v.10 no.2
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• pp.81-88
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• 2009

A non-parametric procedure is presented for testing exponentially against used better than aged in convex ordering class (UBAC) of life distributions based on u-test. Convergence of the proposed statistic to the normal distribution is proved. Selected critical values are tabulated for sample sizes 5(5)40. The Pitman asymptotic relative efficiency of my proposed test to tests of other classes is studied. An example of 40 patients suffering from blood cancer disease demonstrates practical application of the proposed test.

• Journal of the Korean Statistical Society
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• v.27 no.2
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• pp.197-203
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• 1998

To test the hypothesis of complete or total independence for a multi-way contingency table, the Pearson chi-squared test statistic is usually employed under Poisson or multinomial models. It is well known that, under the hypothesis, this statistic follows an asymptotic chi-squared distribution. We consider the case where all marginal sums of the contingency table are fixed. Using conditional limit theorems, we show that the chi-squared test statistic has the same limiting distribution for this case.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2C
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• pp.109-116
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• 2006

Many application scenarios of ultra-wideband(UWB) radio assume non-line-of-sigit(non-LoS) signal propagations. Through-material propagation of UWB signal introduces a distortion of the waveform as well as attenuation, which will introduce a decrease of the correlation coefficient between the correlator template and the received signal. A hypothesis test approach to selection of the template waveform for UWB rake receivers is posed. Linde-Buzo-Gray(LBG) algorithm is used to select the candidate waveforms which are used to setup the hypothesis test. The performance of the algorithm is tested using a set of indoor non-LoS propagation measurement data.

• The Korean Journal of Health Service Management
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• v.13 no.2
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• pp.121-133
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• 2019

Objectives: The purpose of this study was to determine the effectiveness of hand acupressure in relieving shoulder pain among male manufacturing workers. Methods: A nonequivalent control group was employed using a nonsynchronized design. Data were collected from two manufacturing plants located in metropolitan area U. The data were analyzed with a ${\chi}^2$-test and an independent t-test to perform a homogeneity test, and with repeated ANOVA measures to perform an hypothesis test using the SPSS 21.0 program. Results: Hypothesis 1 that "the experimental group provided with hand moxibustion would score lower for low shoulder pain than the control group" was supported (F=306.05, p<.001). Hypothesis 2, that "the experimental group provided with hand moxibustion would have a wider range of motion than the control group" was also supported with increased flexion(F=7.73, p<.001), extension(F=7.89, p=.001), abduction(F=21.23, p<.001), and horizontal abduction(F=20.89, p=.028). Hypothesis 3, that "the experimental group provided with hand moxibustion would score lower for quality of sleep than the control group" was not supported (F=56.10, p=.091). Conclusions: Hand moxibustion is effective in relieving shoulder pain for male manufacturing workers, in relieving low shoulder pain and increasing the range of motion.

• Journal of the Korean Statistical Society
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• v.25 no.1
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• pp.81-94
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• 1996

This paper suggests a new criterion for testing the general linear hypothesis about coefficients in multivariate growth curve model. It is developed from a Bayesian point of view using the highest posterior density region methodology. Likelihood ratio test criterion(LRTC) by Khatri(1966) results as an approximate special case. It is shown that under the simple case of vague prior distribution for the multivariate normal parameters a LRTC-like criterion results; but the degrees of freedom are lower, so the suggested test criterion yields more conservative test than is warranted by the classical LRTC, a result analogous to that of Berger and Sellke(1987). Moreover, more general(non-vague) prior distributions will generate a richer class of tests than were previously available.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.46-51
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• 2007

In this paper, we propose a new data clustering method using local probability and hypothesis theory. To cluster the test data set we analyze the local area of the test data set using local probability distribution and decide the candidate class of the data set using mean standard deviation and variance etc. To decide each class of the test data, statistical hypothesis theory is applied to the decided candidate class of the test data set. For evaluating, the proposed classification method is compared to the conventional fuzzy c-mean method, k-means algorithm and Discriminator analysis algorithm. The simulation results show more accuracy than results of fuzzy c-mean method, k-means algorithm and Discriminator analysis algorithm.

• Communications for Statistical Applications and Methods
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• v.2 no.1
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• pp.112-121
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• 1995

Let $p_1$ and $p_2$ be the proportions of two populations. To test the hypothesis $H_0 : p_1 = p_2$, we usually use the $x^2$ statistic, the large sample binomial statistic Z, and the Generalized Likelihood Ratio statistic-2log $\lambda$developed based on different mathematical rationale, respectively. Since testing the above hypothesis is equivalent to testing whether two populations follow the common Bernoulli distribution, one may also test the hypothesis by comparing 1 with the ratio of each density estimate and the hypothesized common density estimate, called comparison density, which was devised by Parzen(1988). We show that the above traditional test statistics ate actually estimating the measure of distance between the true densities and the common density under $H_0$ by representing them with the comparison density.