• Journal of the Korea Society of Computer and Information
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• v.21 no.4
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• pp.115-121
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• 2016

The purpose of this paper is to study the influence of hypersensitiveness in accident reports and usefulness on admission of atomic power generation. By the result of study we can look forward to understanding obstacles and promotion factors in constructing atomic power plants. The results of hypothesis testing can be summarized as follows: First, the hypothesis that hypersensitiveness in accident reports has a plus effect on reliability is accepted. Secondly, the hypothesis that hypersensitiveness in accident reports has a plus effect on anxiety is accepted. Thirdly, the hypothesis that reliability has a plus effect on anxiety is accepted. Fourthly, the hypothesis that reliability has a minus effect on receptiveness is accepted. Fifthly, the hypothesis that anxiety has a minus effect on receptiveness is accepted. Sixthly, the hypothesis that usefulness has a minus effect on necessity is accepted. Seventhly, the hypothesis that usefulness has a plus effect on receptiveness is rejected. Eighthly, the hypothesis that necessity has a minus effect on receptiveness is accepted.

• Journal of Korean Elementary Science Education
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• v.25 no.2
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• pp.159-166
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• 2006

Changes in the brain activities following pre-service elementary teachers' learning hypothesis-generation were investigated using the analysis of EEG relative power and EEG coherence. In this study, the experimental group (n=16) were trained using learning methods for hypothesis-generation and the control group(n=16) were trained using learning methods for hypothesis-reception over the course of 8 weeks. EEG was measured before and following the learning process for both groups. Decreased theta ($4{\sim}7.9Hz$)/alpha 1 ($8{\sim}9.9Hz$) power and increased alpha 2 ($10{\sim}l2.9Hz$)/beta ($13{\sim}29.9Hz$)/gamma ($30{\sim}50Hz$) power were showed in the experimental group. Additionally, many changes in brian activities were observed for theta, beta and gamma coherence in the experimental group. In particular, fronto-parietal coherence increased in the experimental group. These differences in brain activities between the two groups suggest that the learning for subjects' hypothesis generation presumably leads to interesting changes in some types of brain activities in pre-service elementary teachers.

• Proceedings of the PSK Conference
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• 2003.04a
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• pp.90-90
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• 2003

Sample size calculations play an important role in bioequivalence trials. In almost all clinical trials sample size is determined by considering power under the alternative hypothesis. The alternative hypothesis is the hypothesis that we wish to prove with experiments. Hence, in bioequivalence trials the alternative hypothesis is that two formulations are bioequivalent, while the null hypothesis is that the two formulations are not bioequivalent. (omitted)

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1393-1405
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• 2017

It is well known that duty-cycling control by dynamically adjusting the polling interval according to the traffic loads can effectively achieve power saving in wireless sensor networks. Thus, there has been a significant research effort in developing polling interval adaptation schemes. Especially, Dynamic Low Power Listening (DLPL) scheme is one of the most widely adopted open-looping polling interval adaptation techniques in wireless sensor networks. In DLPL scheme, if consecutive idle (busy) samplings reach a given fixed threshold, the polling interval is increased (decreased). However, due to the trial-and-error based approach, it may significantly deteriorate the system performance depending on given threshold parameters. In this paper, we propose a novel DLPL scheme, called SDL (Sequential hypothesis testing based Dynamic LPL), which employs sequential hypothesis testing to decide whether to change the polling interval conforming to various traffic conditions. Simulation results show that SDL achieves substantial power saving over state-of-the-art DLPL schemes.

• Journal of Arbitration Studies
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• v.30 no.2
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• pp.165-184
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• 2020

In this study, the factors affecting opportunism in the relationship between suppliers and key accounts were analyzed from the viewpoint of transaction cost theory, market power theory, and relationship marketing theory. As a result of the hypothesis test, Hypothesis 1 stating that demand volatility will have a positive effect on opportunism and Hypothesis 2 that transaction-specific investment will have a positive effect on opportunism were also supported. In addition, Hypothesis 3 stating that channel power will have a positive effect on opportunism was also supported. Lastly, Hypothesis 4 stating that relational commitment will have a negative effect on opportunism was not supported, along with Hypothesis 5 stating that transaction satisfaction will have a negative effect on opportunism. The theoretical and practical implications of this study are as follows. This study has identified the antecedents of opportunism by comprehensively applying the transaction cost theory, market power theory, and relationship marketing theory. In addition, this study can identify what a company should manage specifically to lower opportunism by identifying the antecedents of opportunism. The limitations of this study and the directions for future studies are as follows. First, not all of the antecedents of opportunism of key accounts have been extensively investigated from the viewpoint of the transaction cost theory, market power theory, and relationship marketing theory. In the future, it is necessary to identify additional factors. Second, the study was conducted only in the supplier's viewpoint. In future studies, it is expected that more accurate research results can be obtained by simultaneously examining not only the supplier's point of view but also the buyer's point of view.

• Proceedings of the Safety Management and Science Conference
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• 2012.11a
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• pp.461-471
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• 2012

Most useful statistical techniques in six sigma DMAIC are hypothesis testing and interval estimation. So this paper reviews and derives sample size formula by considering significance level, power of detectability and effect difference. The quality practioners can effectively interpret the practical and statistical significance with the rational sample sizing.

• Integrative Medicine Research
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• v.6 no.3
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• pp.254-259
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• 2017

Background: Those classified as Tae-Eum (TE)-type people in Sasang constitutional medicine (SCM) are prone to obesity. Although extensive clinical observations have confirmed this tendency, the underlying physiological mechanisms are unknown. Here, we propose a novel hypothesis using integrative physiology to explain this phenomenon. Methods: Hypoactive lung function in the TE type indicates that respiration is attenuated at the cellular level - specifically, mitochondrial oxygen consumption. Because a functional reduction in cellular energy metabolism is suggestive of intrinsic hypoactivity in the consumption (or production) of metabolic energy, we reasoned that this tendency can readily cause weight gain via an increase in anabolism. Thus, this relationship can be derived from the graph of cellular metabolic power plotted against body weight. We analyzed the clinical data of 548 individuals to test this hypothesis. Results: The statistical analysis revealed that the cellular metabolic rate was lower in TEtype individuals and that their percentage of obesity (body mass index >25) was significantly higher compared to other constitutional groups. Conclusion: Lower cellular metabolic power can be an explanation for the obesity trend in TE type people.

• Journal of Environmental Science International
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• v.13 no.10
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• pp.955-963
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• 2004

This study was conducted to find and explain characteristics of diverse social groups' attitudes toward nuclear power plant. 7 hypotheses were constructed and statistically tested. In order to test 7 hypotheses, both Nuclear Energy Attitude Scale and Environmental Attitude Scale were distributed and field surveyed on 839 respondents representing a diverse range of subject groups. The results showed that 6 hypotheses were statistically accepted while 1 hypothesis statistically rejected. Contrary to the first hypothesis, this test found that people in close proximity to the nuclear power plant had more positive attitudes toward nuclear power than those living in distance from the nuclear power plant. Males had more positive nuclear energy attitudes than females. Academic backgrounds were not related to nuclear energy attitudes. Environmental attitudes showed negative relationship with nuclear energy attitudes. While anti-nuclear citizen action groups had the most negative attitudes, nuclear power plant workers had the most positive attitudes among responding groups. Finally, university students majoring in nuclear engineering had more positive attitudes than those of religious studies.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.483-497
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• 2001

When one fits a parametric density function to a data set, it is usually advisable to test the goodness of the postulated model. In this paper we study the nonparametric tests for testing the null hypothesis against general alternatives, when the null hypothesis specifies the density function up to unknown parameters. We modify the test statistic which was proposed by the first author and his colleagues. Asymptotic distribution of the modified statistic is derived and its performance is compared with some other tests through simulation.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.643-655
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• 2004

L$_1$-estimator in the linear regression model is widely recognized to have superior robustness in the presence of vertical outliers. While the L$_1$-estimation procedures and algorithms have been developed quite well, less progress has been made with the hypothesis test in the multiple L$_1$-regression. This article suggests computer-intensive resampling approaches, jackknife and bootstrap methods, to estimating the variance of L$_1$-estimator and the scale parameter that are required to compute the test statistics. Monte Carlo simulation studies are performed to measure the power of tests in small samples. The simulation results indicate that bootstrap estimation method is the most powerful one when it is employed to the likelihood ratio test.