• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1247-1251
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• 2016

This study aims to investigate the effect of sampling frequency to the time domain and frequency domain analysis of pulse rate variability (PRV). Typical time domain variables - AVNN, SDNN, SDSD, RMSSD, NN50 count and pNN50 - and frequency domain variables - VLF, LF, HF, LF/HF, Total Power, nLF and nHF - were derived from 7 down-sampled (250 Hz, 100 Hz, 50 Hz, 25 Hz, 20 Hz, 15 Hz, 10 Hz) PRVs and compared with the result of heart rate variability of 10 kHz-sampled electrocardiogram. Result showed that every variable of time domain analysis of PRV was significant at 25 Hz or higher sampling frequency. Also, in frequency domain analysis, every variable of PRV was significant at 15 Hz or higher sampling frequency.

• School Mathematics
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• v.16 no.4
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• pp.727-743
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• 2014

The concepts of sample and sampling are central to the statistical thinking and foundations of the statistical literacy, so we need to be emphasized their importance in the statistics education. However, many researches which dealt with samples only analyze textbooks or students' responses. In this study, the concept of sample is addressed by a historical consideration which is one aspect of the didactical analysis. Moreover, developing concept of sample is analyzed from the preceding studies about the statistical literacy, considering the sample representativeness and the sampling variability. The results say that the historical process of developing the concept of sample can be divided into three step: understanding the sample representativeness; appearing the sample variance; recognizing the sampling variability. Above all, it is important to aware and control the sampling variability, but many related researches might not consider sample variability. Therefore, it implies that the awareness and control of sampling variability are needed to reflect to the teaching-learing of sample for developing the students' statistical literacy.

• School Mathematics
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• v.14 no.4
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• pp.495-516
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• 2012

This study distinguished thinking related to statistical variability into six components - the noticing of variability, the explanation of variability, the control of variability, the modeling of variability, the understanding of samples, and the understanding of sampling distribution and investigated the relationships among the thinking components. This study found that this distinction of thinking components related to statistical variability is reasonable. The results showed that each correlation coefficient of the modeling of variability, the understanding of samples, and the understanding of sampling distribution with regard to the noticing of variability, the explanation of variability, and the control of variability is similar. Based on this results, new variable, the understanding of sampling, has been drawn. The results also showed that while the noticing of variability and the control of variability influence the understanding of sampling, the explanation of variability does not influence it.

• School Mathematics
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• v.17 no.3
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• pp.515-530
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• 2015

The concepts of sample and sampling are central to make a statistically correct decision, so we need to be emphasized their importance in the statistics education. Nevertheless, there were not enough studies which discuss how to teach the concepts of sample and sampling. In this study, teaching sample and sampling is addressed by foreign curricula and cases of instruction in order to obtain suggestions for teaching sample and sampling. In particular, the curricular of Australia, New Zealand, England and the United States are analyzed, considering the sample representativeness and the sampling variability; the two elements in the concept of sample. Also foreign textbooks and cases of instruction when it comes to teach sample are analyzed. The results say that with respect to teach sample can be divided into four suggestions: first, sample was taught in the process of statistical inquiry such as data collection, analysis, and results. Second, sample was introduced earlier than Korea curriculum. Third, when it comes to teach sample, sample variability, as well as sample representativeness was considered. Fourth, technological tools were used to enhance understanding sample.

• The Mathematical Education
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• v.57 no.4
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• pp.413-431
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• 2018

In this study, I developed an activity oriented lesson to support the understanding of probabilistic and quantitative estimating population ratios according to the standard statistical principles and discussed its implications in didactical respects. The developed activity lesson, as an efficient physical simulation activity by sampling with replacement, simulates unknown populations and real problem situations through completely closed 'Closed Box' in which we can not see nor take out the inside balls, and provides teaching and learning devices which highlight the representativeness of sample ratios and the sampling variability. I applied this activity lesson to the gifted students who did not learn estimating population ratios and collected the research data such as the activity sheets and recording and transcribing data of students' presenting, and analyzed them by Qualitative Content Analysis. As a result of an application, this activity lesson was effective in recognizing and reflecting on the representativeness of sample ratios and recognizing the random sampling variability. On the other hand, in order to show the sampling variability clearer, I discussed appropriately increasing the total number of the inside balls put in 'Closed Box' and the active involvement of the teachers to make students pay attention to controlling possible selection bias in sampling processes.

• ALGAE
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• v.24 no.2
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• pp.111-120
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• 2009

Choosing an appropriate sample unit is a fundamental decision in the design of ecological studies. While numer-ous methods have been developed to estimate organism abundance, they differ in cost, accuracy and precision.Using both field data and computer simulation modeling, we evaluated the costs and benefits associated with twomethods commonly used to sample benthic organisms in temperatc kelp forests. One of these methods, theTargeted Sampling method, relies on different sample units, each "targeted" for a specific species or group ofspecies while the other method relies on coefficients that represent ranges of bottom cover obtained from visual esti-mates within standardized sample units. Both the field data and the computer simulations suggest that both meth-ods yield remarkably similar estimates of organisnm abundance and among-site variability, although the Coefficientmethod slightly underestimates variability armong sample units when abundances are low. In contrast, the twomethods differ considerably in the effort needed to sample these communities; the Targeted Sampling requiresmore time and twice the persormel to complete. We conclude that the Coeffident Sampling metliod may be bettcrfor environmental monitoring programs where changes in mean abundance are of central conccm and resources arelimiting, but that the Targeted sampling methods may be better for ecological studies where quantitative reIation-ships among species and small-scale variability in abundance are of central concern.

• The Mathematical Education
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• v.56 no.1
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• pp.19-39
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• 2017

Taking samples of data and using samples to make inferences about unknown populations are at the core of statistical investigations. So, an understanding of the nature of sample as statistical thinking is involved in the area of statistical literacy, since the process of a statistical investigation can turn out to be totally useless if we don't appreciate the part sampling plays. However, the conception of sampling is a scheme of interrelated ideas entailing many statistical notions such as repeatability, representativeness, randomness, variability, and distribution. This complexity makes many people, teachers as well as students, reason about statistical inference relying on their incorrect intuitions without understanding sample comprehensively. Some research investigated how the concept of a sample is understood by not only students but also teachers or preservice teachers, but we want to identify preservice secondary mathematics teachers' understanding of sample as the statistical literacy by a qualitative analysis. We designed four items which asked preservice teachers to write their understanding for sampling tasks including representativeness and variability. Then, we categorized the similar responses and compared these categories with Watson's statistical literacy hierarchy. As a result, many preservice teachers turned out to be lie in the low level of statistical literacy as they ignore contexts and critical thinking, expecially about sampling variability rather than sample representativeness. Moreover, the experience of taking statistics courses in university did not seem to make a contribution to development of their statistical literacy. These findings should be considered when design preservice teacher education program to promote statistics education.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.4
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• pp.377-386
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• 2003

In order to develop a confident sampling technique, we designed and constructed a 6-port manifold MFC sampling system for collecting gaseous pollutants in air. Using this instrumentation, we tested the performance criteria of MFC system in terms of: (1) flow rate; (2) MFC-to-MFC variability; (3) tube-to-tube variability; and (4) time. It was interesting to find that the later two factors did not show any significant variations, while the former two show substantially large variations. However, as most of those variabilities are consistent enough to form systematic patterns, we were able to explain the occurrence patterns of all those MFC biases in terms of those four major variables. The overall results of our experiment suggest that one needs to use correction factor for each MFC unit under a given flow rate to maintain optimal accuracy and precision for sampling of those pollutants.

• Journal of Educational Research in Mathematics
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• v.21 no.1
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• pp.17-32
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• 2011

This study investigated pre-service teachers' understanding of statistical sampling. The researchers categorized major topics related to sampling into representativeness of samples, sampling variability, and sampling distribution, and selected concepts connected to each topic. Findings on this study are as follows: Even though most of the pre-service teachers considered the random sampling bringing unbiased outcomes as a proper sampling method, only 64% of them recognized that sample is a quasi-proportional, small-scale version of population; Few pre-service teachers understood that more important is the size of sample, not the portion of sample to population, and half of them appreciated that the number of sampling has a powerful effect on drawing of reliable results than the size of sample; Few pre-service teachers understood that sampling distribute is irrelevant to the shape of population and has a symmetrical bell-shape.

• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.1-20
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• 2009

The present contribution addresses uncertainty quantification and uncertainty propagation in structural mechanics using stochastic analysis. Presently available procedures to describe uncertainties in load and resistance within a suitable mathematical framework are shortly addressed. Monte Carlo methods are proposed for studying the variability in the structural properties and for their propagation to the response. The general applicability and versatility of Monte Carlo Simulation is demonstrated in the context with computational models that have been developed for deterministic structural analysis. After discussing Direct Monte Carlo Simulation for the assessment of the response variability, some recently developed advanced Monte Carlo methods applied for reliability assessment are described, such as Importance Sampling for linear uncertain structures subjected to Gaussian loading, Line Sampling in linear dynamics and Subset simulation. The numerical example demonstrates the applicability of Line Sampling to general linear uncertain FE systems under Gaussian distributed excitation.