• Journal of The Korean Association of Information Education
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• v.25 no.3
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• pp.459-469
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• 2021

In this paper, a case study of basic data science practice education for field teachers and pre-service teachers was studied. In this paper, for basic data science education, spreadsheet software was used as a data collection and analysis tool. After that, we trained on statistics for data processing, predictive hypothesis, and predictive model verification. In addition, an educational case for collecting and processing thousands of public big data and verifying the population prediction hypothesis and prediction model was proposed. A 34-hour, 17-week curriculum using a spreadsheet tool was presented with the contents of such basic education in data science. As a tool for data collection, processing, and analysis, unlike Python, spreadsheets do not have the burden of learning program- ming languages and data structures, and have the advantage of visually learning theories of processing and anal- ysis of qualitative and quantitative data. As a result of this educational case study, three predictive hypothesis test cases were presented and analyzed. First, quantitative public data were collected to verify the hypothesis of predicting the difference in the mean value for each group of the population. Second, by collecting qualitative public data, the hypothesis of predicting the association within the qualitative data of the population was verified. Third, by collecting quantitative public data, the regression prediction model was verified according to the hypothesis of correlation prediction within the quantitative data of the population. And through the satisfaction analysis of pre-service and field teachers, the effectiveness of this education case in data science education was analyzed.

• Proceedings of the Korean Association for Survey Research Conference
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• 2003.06a
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• pp.34-41
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• 2003

In many cases, the measurement error variances may be functions of the unknown true values or related covariates. This paper develops estimators of the parameters of a linear measurement error variance function based on wi thin-unit sample variaoces. This paper devotes to: (1) define measurement error scale factor $\delta$: (2) develop estimators of the parameters of the 1inear measurement error variance function under stratified multistage sampling design and small error conditions; (3) use propensity methods to adjust survey weights to account for possible selection effects at the replicate level. The proposed methods are applied to medical examination data from the U S Third National Health and Nutrition Examination Survey(NHANES III)

• Survey Research
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• v.11 no.3
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• pp.103-114
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• 2010

It is difficult to obtain the population information of target variables when a new sampling design for successive survey is executed. In a research of the Korea National Tourism Survey, we propose a method for selection of efficient stratification variables which are found in a combination of a existing sample data and a new frame list. At first, if there isn't common identification number between the frame list and the sample data, we find a device to substitute for absence of identification number. At second, we suggest a method to search stratification variables correlated with target variables using statistical methods like regression analysis.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.141-150
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• 1998

In this paper, we developed one-stage cluster randomized response model for obtaining quantitative data by using the Greenberg et al. model(1971) when the population was made up of sensitive quantitative clusters. We obtained the minimum variance by calculating the cluster's size and the optimum number of sample clusters under the some given constant cost. We compared the efficiency of our model with the Greenberg et al. model by simple random sampling.

• The Korean Journal of Applied Statistics
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• v.8 no.1
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• pp.1-7
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• 1995

In sample survey for the estimation of low proportion, usually a large size of sample is required for a meaningful estimator. If the cost of a sample unit is high, we have to make every effort to improve the precision of the estimator. In this study, a new efficient allocation method of sample size in stratified sampling is proposed provided we have some prior information for the stratification.

• Journal of Korea Multimedia Society
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• v.4 no.1
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• pp.37-45
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• 2001

The studies to estimate the surface spectral reflectance of an object have received widespread attention using the multi-spectral camera system. However, the multi-spectral camera system requires the additional color filter according to increment of the channel and system complexity is increased by multiple capture. Thus, this paper proposes an algorithm to reduce the estimation error of surface spectral reflectance with the conventional 3-band RGB camera. In the proposed method, adaptive principal components for each pixel are calculated by renewing the population of surface reflectances and the adaptive principal components can reduce estimation error of surface spectral reflectance of current pixel. To evaluate performance of the proposed estimation method, 3-band principal component analysis, 5-band wiener estimation method, and the proposed method are compared in the estimation experiment with the Macbeth Color Checker. As a result, the proposed method showed a lower mean square error between the estimated and the measured spectra compared to the conventional 3-band principal component analysis method and represented a similar or advanced estimation performance compared to the 5-band wiener method.

• Korean Journal of Construction Engineering and Management
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• v.16 no.5
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• pp.105-113
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• 2015

In order to evaluate the energy performance of buildings, it is important to determine reliable information of energy utilization during operating phase. It is also required to establish the database which have the amounts of population deducted by objective filtering and analysis. In this study, all energy sources were applied to evaluate synthetically the energy performance of Multi-Family Housings(MFH) using district heating and the amounts of population were obtained sufficiently through data searching in Seoul and neighbor cities. Finally the database of source energy utilization and $CO_2$ emission in 325 MFH complexes were established through cross validation between data sources and statistical analysis. Additionally, further ideas have been proposed to improve existing MFH Management Information System.

• Economic and Environmental Geology
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• v.53 no.2
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• pp.167-181
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• 2020

Analysis of variance (ANOVA) and multiple comparison analysis were performed for shear strengths categorized by breccia content of 5 wt.% (Case-I), 10 wt.%, (Case-II) and 15 wt.% (Case-III) in fault cores. The relationship between breccia contetnt and shear strength was quantitatively classified by calculating the mean and standard deviation of shear strength for each population in each case and then the grouping the breccia contents that had a statistically similar effect on the dispersion of shear strength. As a result, shear strength was clearly classified into group 1 (breccia content of 0-15 wt.%) and group 2 and 3 (breccia coantent of 15-30 wt.% and 30 wt.% or more) in Case-III. Shear strength of the standard line at breccia content of 15 wt.% were determined to be 43.6 kPa, 77.6 kPa, and 118.6 kPa at each normal stress (54 kPa, 108 kPa, and 162 kPa), respectively. In addition, the distribution range of cohesions is 0-43.6 kPa at breccia content of 15 wt.% or less, and 0-70.0 kPa at 15 wt.% or more. Distribution range of friction angles is 0-45.7 ° at breccia content of 15 wt.% or less, and 16.7-57.5 ° at 15 wt.% or more.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.33 no.1
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• pp.22-30
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• 2023

Currently, almost all product development in the jewelry industry utilizes 3D CAD and 3D printing. In this situation, 3D CAD modeling and 3D printing ability units in colleges, Tomorrow Learning Card Education, and Course Evaluation-type jewelry design related education are conducted with developed curriculum based on the standards for training standards, training hours, training equipment, and practice materials presented by NCS. Accordingly, this study analyzes 3D CAD modeling and 3D printing training facilities, training hours, training equipment, etc into three categories of NCS precious metal processing and jewelry design, and studies the development of educational systems such as 3D CAD/3D printing curriculum and various environments that meet these standards. Education using this 3D CAD/3D printing education system will enable us to continuously supply professional talent with practical skills not only in the jewelry industry but also in the entire 3D CAD/3D printing manufacturing industry, which is called as one of the pillars of the 4th Industry. The quality of employment of trainees receiving education and the long-term retention rate after employed can also have a positive effect. In addition, excellent educational performance will help improve the recruitment rate of new students in jewelry jobs or manufacturing-related departments, which are difficult to recruit new students in recent years.

• Survey Research
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• v.7 no.2
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• pp.53-69
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• 2006

Weights can be made and imposed in both sample design stage and analysis stage in a sample survey. While in design stage weights are related with sample data acquisition quantities such as sample selection probability and response rate, in analysis stage weights are connected with external quantities, for instance population quantities and some auxiliary information. The final weight is the product of all weights in both stage. In the present paper, we focus on the weight in analysis stage and investigate the effect of such weights imposed on the weighted mean when estimating the population mean. We consider a finite population with a pair of fixed survey value and weight in each unit, and suppose equal selection probability designs. Under the condition we derive the formulas of the bias as well as mean square error of the weighted mean and show that the weighted mean is biased and the direction and amount of the bias can be explained by the correlation between survey variate and weight: if the correlation coefficient is positive, then the weighted mein over-estimates the population mean, on the other hand, if negative, then under-estimates. Also the magnitude of bias is getting larger when the correlation coefficient is getting greater. In addition to theoretical derivation about the weighted mean, we conduct a simulation study to show quantities of the bias and mean square errors numerically. In the simulation, nine weights having correlation coefficient with survey variate from -0.2 to 0.6 are generated and four sample sizes from 100 to 400 are considered and then biases and mean square errors are calculated in each case. As a result, in the case or 400 sample size and 0.55 correlation coefficient, the amount or squared bias of the weighted mean occupies up to 82% among mean square error, which says the weighted mean might be biased very seriously in some cases.