• 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.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.163-165
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• 2003

Some multivariate analyses provide configurations for variables or objects in low dimensional space because we can see easily their relation. In particular, in simple correspondence analysis(SCA), we can obtain the various configurations which are called SCA Maps based on the algebraic algorithms. Moreover, it often occur the variability among them. Therefore, in this study, we will give a comparison of variability of SCA maps using the procrustes analysis which is a technique of comparing configurations in multidimensional scaling.

• Journal of Gifted/Talented Education
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• v.23 no.3
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• pp.387-406
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• 2013

This study compared levels of mathematically talented students' statistical thinking with those of non-talented students in the noticing of statistical variability. t tests were conducted to test for statistically significant differences between mathematically gifted students and non-gifted students. Results for the t-test shows that there is no difference between the TE students' and NE students' noticing of variability in the measurement settings. Meanwhile, the t-test results also show that there is a difference between the TM students' and NM students' noticing of variability in the both measurement and chance settings. 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. These results are thought-provoking results in statistics instruction for mathematically talented students.

• Ocean Systems Engineering
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• v.7 no.4
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• pp.319-328
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• 2017

Higher prediction efficacy is a very challenging task in any field of engineering. Due to global warming, there is a considerable increase in the global sea level. Through this work, an attempt has been made to find the sea level variability due to climate change impact at Haldia Port, India. Different statistical downscaling techniques are available and through this paper authors are intending to compare and illustrate the performances of three regression models. The models: Wavelet Neural Network (WNN), Minimax Probability Machine Regression (MPMR), Feed-Forward Neural Network (FFNN) are used for projecting the sea level variability due to climate change at Haldia Port, India. Model performance indices like PI, RMSE, NSE, MAPE, RSR etc were evaluated to get a clear picture on the model accuracy. All the indices are pointing towards the outperformance of WNN in projecting the sea level variability. The findings suggest a strong recommendation for ensembled models especially wavelet decomposed neural network to improve projecting efficiency in any time series modeling.

• Communications for Statistical Applications and Methods
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• v.18 no.1
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• pp.1-12
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• 2011

We analyzed the observed temperature data for 50 years on 5 representative points in Korea to verify global warming and the increase in climate variability. We found that there was some level of global warming but we could not disregard the effects of urbanization. In addition, we could not find any information for the increase in climate variability.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.599-610
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• 2018

The goal of this study was to statistically analyse the variability of global irradiance and ultraviolet erythemal (UVER) irradiance and their interrelationships with global and UVER irradiance, global clearness indices and ozone. A prediction of short-term UVER solar irradiance values was also obtained. Extreme values of UVER irradiance were included in the data set, as well as a time series of ultraviolet irradiance variability (UIV). The study period was from 2005 to 2014 and approximately 250,000 readings were taken at 5-min intervals. The effect of the clearness indices on global irradiance variability (GIV) and UIV was also recorded and bi-dimensional distributions were used to gather information on the two measured variables. With regard to daily GIV and UIV, it is also shown that for global clearness index ($k_t$) values lower than 0.6 both global and UVER irradiance had greater variability and that UIVon cloud-free days ($k_t$ higher than 0.65) exceeds GIV. To study the dependence between UIVand GIV the ${\chi}^2$ statistical method was used. It can be concluded that there is a 95% probability of a clear dependency between the variabilities. A connection between high $k_t$ (corresponding to cloudless days) and low variabilities was found in the analysis of bidimensional distributions. Extreme values of UVER irradiance were also analyzed and it was possible to calculate the probable future values of UVER irradiance by extrapolating the values of the adjustment curve obtained from the Gumbel distribution.

• The Journal of Korean Obstetrics and Gynecology
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• v.24 no.4
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• pp.50-61
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• 2011

Purpose: This study was performed to analyse autonomic nerve function of some examinees with dysmenorrhea by comparing the characteristics of dysmenorrhea patients' Heart Rate Variability(HRV) in examinees group with non-examinees group. Methods: We studied 110 patients visiting OO Korean hospital to take medical treatment for dysmenorrhea from September, 2006 to August, 2010. The subjects were categorized in two groups, 36 examinees and 57 non-examinees controls. We investigated the difference of HRV between two groups by student's T-test using SPSS for Windows(version 12.0). Results: 1. SDNN in examinees group is statistical significantly lower than non-examinees group. RMS-SD in examinees group is lower than non-examinees group non-statistical significantly. Mean PR in examinees group is higher than non-examinees group non-statistical significantly. 2. VLF, ln TP, ln VLF and ln HF are statistically significantly lower than non-examinees group. TP, HF and ln LF are lower than non-examinees group non-statistical significantly. LF is higher than non-examinees group non-statistical significantly. Conclusions: The results suggest that stress is related to especially examinees in dysmenorrhea patients by effecting autonomic nerve function. HRV can be useful to diagnose examinees' dysmenorrhea. The further studies would be needed to study about relation between stress, dysmenorrhea and HRV for settling this.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.435-446
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• 2000

Statistical process control(SPC) and engineering process control(EPC) are two strategies for quality improvement that have been developed independently. EPC seeks to minimize variability by adjusting compensatory variables in order to make the process level close to the target, while SPC seeks to reduce variability by monitoring and eliminating causes of variation. One purpose of this paper is to propose the IMA(0,1,1) model as the in-control process model. For the out-of-control process model we consider two cases; one is the case with a step shift in the level, and the other is the case with a change in the nonstationarity. Another purpose is to suggest the use of an integrated process control procedure with adjustment and monitoring, which can consider the proposed process model effectively. An integrated control procedure will improve the process control activity significantly for cases of the proposed model, when compared to the procedure of using either EPC or SPC, since EPC will keep the process close to the target and SPC will eliminate special causes.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.49-60
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• 2021

BCSA (Bias-Correction and Stochastic Analog) is a statistical downscaling technique designed to effectively correct the systematic errors of GCM (General Circulation Model) output and reproduce basic statistics and spatial variability of the observed precipitation filed. In this study, the applicability of BCSA was evaluated using the ASOS observation data over South Korea, which belongs to the monsoon climatic zone with large spatial variability of rainfall and different rainfall characteristics. The results presented the reproducibility of temporal and spatial variability of daily precipitation in various manners. As a result of comparing the spatial correlation with the observation data, it was found that the reproducibility of various climate indices including the average spatial correlation (variability) of rainfall events in South Korea was superior to the raw GCM output. In addition, the needs of future related studies to improve BCSA, such as supplementing algorithms to reduce calculation time, enhancing reproducibility of temporal rainfall patterns, and evaluating applicability to other meteorological factors, were pointed out. The results of this study can be used as the logical background for applying BCSA for reproducing spatial details of the rainfall characteristic over the Korean Peninsula.

• Proceedings of the Korean Society for Quality Management Conference
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• 2006.11a
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• pp.211-216
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• 2006

Monitoring variability is a vital part of modem statistical process control. The conventional Shewhart Rand S charts address the setting where the in-control process readings have a constant variance. In some settings, however, it is the coefficient of variation, rather than the variance, that should be constant. This paper develops a chart, equivalent to the S chart, for monitoring the coefficient of variation using rational groups of observations.