• The Mathematical Education
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• v.59 no.1
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• pp.47-62
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• 2020

Understanding the concept of probability distribution becomes more important. We considered probabilities defined in the sample space, the definition of discrete random variables, the probability of defined discrete probability distribution, and the relationship between them as knowledge of discrete probability distribution, and investigated the understanding degree of the mathematics preservice teachers. The results are as follows. Firstly, about 70% of preservice teachers who participated in this study expressed discrete probability distribution graphs in ordered pairs or continuous distribution. Secondly, with regard to the two factors for obtaining discrete probability distributions: probability for each element in the sample space and the concept of random variables that convert each element in the sample space into a real value, only 13% of the preservice teachers understood and addressed both factors. Thirdly, 39% of the preservice teachers correctly responded to whether different probability distributions can be defined for one sample space. Fourthly, when the probability of each fundamental event was determined to obtain the probability distribution of the discrete random variables defined in the undefined sample space, approximately 70% habitually calculated by the uniform probability. Finally, about 20% of preservice teachers understood the meaning and relationship of binomial distribution, discrete random variables, and sample space. In relation, clear definitions and full explanations of concept need to be provided from textbooks and a program to improve the understanding of preservice teachers need to be developed.

• Journal of the Korean Statistical Society
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• v.19 no.2
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• pp.122-127
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• 1990

Consider the following problem in the large deviation theory. For constants $a_1, \cdots, a_p$ the tail probability $P(M_1 > a_1, \cdots, M_p > a_p)$ of the sample medians $(M_1, \cdots, M_p)$ is supposed to converge to zero as sample size increases. This paper shows that this probability converges to zero exponentially fast and estimates the convergence rates of the above tail probability of the sample medians. Also compare with the rates about the sample means.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.1
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• pp.15-24
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• 2006

In simulation input modeling, it is important to identify a probability distribution to represent the input process of interest. In this paper, an appropriate sample size is determined for parameter estimation associated with some typical probability distributions frequently encountered in simulation input modeling. For this purpose, a statistical measure is proposed to evaluate the effect of sample size on the precision as well as the accuracy related to the parameter estimation, square rooted mean square error to parameter ratio. Based on this evaluation measure, this sample size effect can be not only analyzed dimensionlessly against parameter's unit but also scaled regardless of parameter's magnitude. In the Monte Carlo simulation experiments, three continuous and one discrete probability distributions are investigated such as ; 1) exponential ; 2) gamma ; 3) normal ; and 4) poisson. The parameter's magnitudes tested are designed in order to represent distinct skewness respectively. Results show that ; 1) the evaluation measure drastically improves until the sample size approaches around 200 ; 2) up to the sample size about 400, the improvement continues but becomes ineffective ; and 3) plots of the evaluation measure have a similar plateau pattern beyond the sample size of 400. A case study with real datasets presents for verifying the experimental results.

• Journal of the Korean Data and Information Science Society
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• v.15 no.3
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• pp.523-529
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• 2004

In Korea, mathematics education has been changed according to the 7th national mathematics curriculum renovated by the Ministry of Education and Human Resources Development announcement in 1997. The education of probability and Statistics has been carried out as a part of this curriculum. We analyze and compare 3 kinds of mathematics textbooks for 10-12 grade students. Descriptions of random variable, sample variance and sample standard deviation, distribution of sample mean, and etc. which are on some textbooks, are misleaded in school education. We suggest the unbiased estimator of sample variance in textbooks and distributions of sample means with normal population assumption.

• Education of Primary School Mathematics
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• v.14 no.1
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• pp.69-79
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• 2011

The purpose of this study is to analyze 3rd, 4th and 5th graders' probability understanding and raise issues concerning instructional methods and search for the possibility of learning probability. For the purpose, a descriptive study through pencil-and-paper test regarding fairness, sample space, probability of event, probability comparison, independence and conditional probability was conducted. The following conclusions were drawn from the results obtained in this study. First, the 3rd, 4th, and 5th grade students scored the highest in the sample space questions. In descending order of skill, the students scored the highest in sample space following probability of events, fairness and probability comparison. Second, however, the level of independence understanding was low. There was no meaningful differences between grades and the conditional probability was the least understood. The independence is difficult to develop naturally according to cognitive development. The conditional probability recognizing the probability of an event changes in non-replacement situations was very difficult for these students. Third, there were significant differences between the 5th graders and the 3rd and 4th graders in the probability comparison questions. It shows that 5th graders understand the concept of proportion when they compare equal ratio probability of an event. The 3rd graers could do different ratio probability of an event more easily than equal ratio probability of an event after they were instructed on probability comparison.

• Journal of the Korean Statistical Society
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• v.13 no.2
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• pp.87-106
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• 1984

It ${X_t}$ is a sequence of independent identically distributed normal random variables, then the conditional probability density of $X_1, X_2, \cdots, X_n$ given the first p+1 sample autocovariances converges to the maximum entropy probability density satisfying the corresponding covariance constraints as the length of the sample sequence tends to infinity. This establishes that the maximum entropy probability density and the associated Gaussian autoregressive process arise naturally as the answers of conditional limit problems.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1317-1325
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• 2016

The purpose of this paper is to explain the factors affecting the wage of the vocational high school graduates. We particularly examine the effectiveness of controlling sample selection bias by employing the Tobit model and Heckman sample selection model. The major results are as follows. First it is shown that the Tobit model and Heckman sample selection model controlling sample selection bias is statistically significant. Hence all the independent variables seem to be statistically consistent with the theoretical model. Second, gender was statistically significant, both in the probability of employment and the wage. Third, the employment probability and wage of Maester high school graduates were shown to be high compared to all other graduates. Fourth, the higher parent's income, the higher are both the employment probability and the wage. Finally, parents education level, high school grade, satisfaction, and a number of licenses were found to be statistically significant, both in the probability of employment and wages.

• Journal of the Korean Statistical Society
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• v.24 no.2
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• pp.563-568
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• 1995

It is well-known that the sample mean and the sample variance are jointly sufficient under normality assumption. In this paper a proof of the joint sufficiency is given without using the factorization criterion. It is related to a finite Sanov-type conditional theorem, i.e., the conditional probability density of $Y_1$ given sample mean $\mu$ and sample variance $\sigma^2$, where $Y_1, Y_2, \cdots, Y_n$ are independently and identically distributed (i.i.d.) normal random variables with mean m and variance $\delta^2$, equals that of $Y_1$ given sample mean $\mu$ and sample variance $\sigma^2$, where $Y_1, Y_2, \cdots, Y_n$ are i.i.d. normal random variables with mean $\mu$ and variance $\sigma^2$.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.10C
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• pp.1027-1035
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• 2005

In this paper, the performance of the sequential detection scheme proposed in Part 1 is compared with that of the fixed sample size (FSS) test, sequential probability ratio test (SPRT), and truncated sequential probability ratio test (TSPRT). The proposed sequential detection scheme requires less complexity and, in most cases, smaller sample size than the SPRT. It is also observed that the proposed sequential detection scheme has always lower complexity and smaller sample size than the FSS test and TSPRT.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.263-275
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• 2022

Use of appropriate technique for non-response occurring in sample survey improves the accuracy of the estimation. Many studies have been conducted for handling non-ignorable non-response and commonly the response probability is estimated using the propensity score method. Recently, post-stratification method to obtain the response probability proposed by Chung and Shin (2017) reduces the effect of bias and gives a good performance in terms of the MSE. In this study, we propose a new response probability estimation method by combining the propensity score adjustment method using the logistic regression model with post-stratification method used in Chung and Shin (2017). The superiority of the proposed method is confirmed through simulation.