• Journal of the Korean School Mathematics Society
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• v.9 no.3
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• pp.309-316
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• 2006

This study examined the ambiguous using the terminology of statistics at mathematics textbook of highschool in Korea and proposed the correct using of sampling error and sampling distribution of sample mean with consistency. And this paper proposed that the concept of error have to teach in context of sampling action in school mathematics.

• School Mathematics
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• v.12 no.3
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• pp.273-299
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• 2010

The purpose of the study is designing and implementing 'Sampling Distributions Simulation' to help students to understand concepts of sampling distributions. This computer simulation is developed to help students understand sampling distributions more easily. 'Sampling Distributions Simulation' consists of 4 sessions. 'The first session - Confidence level and confidence intervals - includes checking if the intended confidence level is actually achieved by the real relative frequency for the obtained sample confidence intervals containing population mean. This will give the students clearer idea about confidence level and confidence intervals in addition to the role of sampling distribution of the sample means among those. 'The second session - Sampling Distributions - helps understand sampling distribution of the sample means, through the simulation method to make comparison between the histogram of sampling distributions and that of the population. The third session - The Central Limit Theorem - includes calculating the means of the samples taken from a population which follows a uniform distribution or follows a Bernoulli distribution and then making the histograms of those means. This will provides comprehension of the central limit theorem, which mentions about the sampling distribution of the sample means when the sample size is very large. The forth session - the normal approximation to the binomial distribution - helps understand the normal approximation to the binomial distribution as an alternative version of central limit theorem. With the practical usage of the shareware 'Sampling Distributions Simulation', we expect students to have a new vision on the sampling distribution and to get more emphasis on it. With the sound understandings on the sampling distributions, more accurate and profound statistical inferences are expected. And the role of the sampling distribution in the inferences should be more deeply appreciated.

• 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.

• Journal of Gifted/Talented Education
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• v.22 no.3
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• pp.503-525
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• 2012

This study compared levels of mathematically talented students' statistical thinking with those of non-talented students in statistical modeling and sampling distribution understanding. t tests were conducted to test for statistically significant differences between mathematically gifted students and non-gifted students. In case of statistical modeling, for both of elementary and middle school graders, the t tests show that there is a statistically significant difference between mathematically gifted students and non-gifted students. Table of frequencies of each level, however, shows that levels of mathematically gifted students' thinking were not distributed at the high levels but were overlapped with those of non-gifted students. A similar tendency is also present in sampling distribution understanding. These results are thought-provoking results in statistics instruction for mathematically talented students.

• 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.

• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.94-99
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• 2015

With the recent machine learning paradigm of using nonparametric Bayesian statistics and statistical inference based on random sampling, the Dirichlet distribution finds many uses in a variety of graphical models. It is a multivariate generalization of the gamma distribution and is defined on a continuous (K-1)-simplex. This paper presents a sampling method for a Dirichlet distribution for the problem of dividing an integer X into a sequence of K integers which sum to X. The target samples in our problem are all positive integer vectors when multiplied by a given X. They must be sampled from the correspondingly gridded simplex. In this paper we develop a Markov Chain Monte Carlo (MCMC) proposal distribution for the neighborhood grid points on the simplex and then present the complete algorithm based on the Metropolis-Hastings algorithm. The proposed algorithm can be used for the Markov model, HMM, and Semi-Markov model for accurate state-duration modeling. It can also be used for the Gamma-Dirichlet HMM to model q the global-local duration distributions.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.273-282
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• 2003

The problem of estimating the parameters of k-population Weibull distributions is discussed under the prior of ordered scale parameters. Parameters are estimated by the Gibbs sampling method. Since the conditional posterior distribution of the shape parameter in the Gibbs sampler is not log-concave, the shape parameter is generated by the adaptive rejection sampling. Finally, we applied this estimation methodology to the data discussed in Nelson (1970).

• The Korean Journal of Applied Statistics
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• v.26 no.5
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• pp.713-723
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• 2013

An objective Bayesian estimation procedure of the two-parameter Pareto distribution is presented under the reference prior and the noninformative prior. Bayesian estimators are obtained by Gibbs sampling. The steps to generate parameters in the Gibbs sampler are from the shape parameter of the gamma distribution and then the scale parameter by the adaptive rejection sampling algorism. A numerical study shows that the proposed objective Bayesian estimation outperforms other estimations in simulated bias and mean squared error.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.459-466
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• 2011

This article addresses robust Bayesian modeling for meta analysis which derives general conclusion by combining independently performed individual studies. Specifically, we propose hierarchical Bayesian models with unknown variances for meta analysis under priors which are scale mixtures of normal, and thus have tail heavier than that of the normal. For the numerical analysis, we use the Gibbs sampler for calculating Bayesian estimators and illustrate the proposed methods using actual data.

• Journal of the Korean School Mathematics Society
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• v.26 no.1
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• pp.21-47
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• 2023

This study examines the activity system of teaching and learning about informal statistical inference using sampling simulation, based on cultural-historical activity theory. The research explores what contradictions arise in the activity system and how the system changes as a result of these contradictions. The participants were 20 elementary school students in the 5th to 6th grades who received classes on informal statistical inference using sampling simulations. Thematic analysis was used to analyze the data. The findings show that a contradiction emerged between the rule and the object, as well as between the mediating artifact and the object. It was confirmed that visualization of empirical sampling distribution was introduced as a new artifact while resolving these contradictions. In addition, contradictions arose between the subject and the rule and between the rule and the mediating artifact. It was confirmed that an algorithm to calculate the mean of the sample means was introduced as a new rule while resolving these contradictions.