• Journal of Environmental Health Sciences
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• v.26 no.2
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• pp.34-40
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• 2000

The purpose of this study was to investigate the seasonal variation of PAHs and to estimate their source characteristics in Taegu area. To do this, four sampling sites were selected to represent an industrial, a traffic, a traffic & residential, and a residential area in Taegu. Total of 72 samples had been collected from January, 1999 to September, 1999 on glass micro fiber filters by high volume air sampler. The PAHs in the total suspended particulate were extracted by a soxhlet process with dichloromethane and analyzed by GC/MSD, GC/FID. A statistical analysis was performed for the PAHs data set using a principal component analysis to derive important factor inherent in the interactions among the variables. The specific conclusions of this research are: 1) There was a significant seasonal and local variation in the atmospheric concentration of PAHs. The seasonal variation is winter>spring>Fall>summer, and the local variation is industrial>traffic>graffic & residential>residential area. 2) To evaluate the correlation between a measured PAHs and other affecting factors such as air pollutant concentration and meterological data, statistical analysis was performed. PAHs and other affecting factors such as air pollutant concentration and meterological data, statistical analysis was performed. PAHs have negative correlation with temperature (r=-0.593, p<0.05), radiation(r=-0.535, p<0.05), and O3(r=-0.719, p<0.05), but have positive correlation with NO(r=0.615, p<0.05) 3)Finally, multivariate analysis was performed for the PAHs dat set to identify and to estimate the source contributions of PAHs. According to results of statistical analysis, it could be identifies as three factors such as vehicular/gasoline, vehicular/diesel, and combustion in Taegu area.

• Communications for Statistical Applications and Methods
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• v.17 no.2
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• pp.193-204
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• 2010

Recently, researchers of the various fields where the spatial analysis is needed have more interested in spatial statistics. In case of data with spatial correlation, methodologies accounting for the correlation are required and there have been developments in methods for spatial data analysis. Lattice data among spatial data is analyzed with following three procedures: (1) definition of the spatial neighborhood, (2) definition of spatial weight, and (3) the analysis using spatial models. The present paper shows a spatial statistical analysis method superior to a general statistical method in aspect estimation by using the trimmed mean squared error statistic, when we analysis the spatial lattice data that outliers are included. To show validation and usefulness of contents in this paper, we perform a small simulation study and show an empirical example with a criminal data in BusanJin-Gu, Korea.

• 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.

• Bulletin of the Society of Naval Architects of Korea
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• v.26 no.4
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• pp.14-26
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• 1989

This paper describes the method of statistical analysis and its programs for predicting the ship's powering performance. The equation for the wavemaking resistance coefficient is derived as the sectional area coefficients by using the wavemaking resistance theory and its regression coefficients are determined from the regression analysis of the model test results. The equations for the form factor, wake franction and thrust deduction fraction are derived by purely regression analysis of the principal dimensions, sectional area coefficients and model test results. The statistical analyses are performed using the various descriptive statistic and stepwise regression analysis techniques. The powering performance prognosis program is developed to cover the prediction of resistance coefficients, propulsive coefficients, propeller open-water efficiency and various scale effect corrections.

• Korean Journal of Child Studies
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• v.37 no.5
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• pp.129-144
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• 2016

Objective: This study's purpose was to develop a teaching competency scale for character education for early childhood teachers and to examine its reliability. Methods: The study procedure was as follows: First, scale items were generated by reviewing the literature as a deductive approach and focused interviews with 8 field experts in an inductive approach. Second, face validation was assessed by 7 academic experts, and statistical validation processes were done twice. The data used in the final statistical analyses included 206 questionnaires for early childhood teachers. Statistical validation included item response analysis, item discrimination analysis, exploratory factor analysis, confirmatory factor analysis, and reliability analysis. Results: The 3 factors identified were as follows: Communicate teaching competence, practical teaching competence, and instrumental teaching competence. The validity and reliability of the Teaching Competency Scale for Character Education in Early Childhood Teachers were acceptable. Conclusion: This new scale will be a useful tool to better support character education for early childhood teachers and contribute to providing a basis for developing more sophisticated tools.

• BMB Reports
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• v.40 no.2
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• pp.226-231
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• 2007

Housekeeping genes are widely used as internal controls in a variety of study types, including real time RT-PCR, microarrays, Northern analysis and RNase protection assays. However, even commonly used housekeeping genes may vary in stability depending on the cell type or disease being studied. Thus, it is necessary to identify additional housekeeping-type genes that show sample-independent stability. Here, we used statistical analysis to examine a large human microarray database, seeking genes that were stably expressed in various tissues, disease states and cell lines. We further selected genes that were expressed at different levels, because reference and target genes should be present in similar copy numbers to achieve reliable quantitative results. Real time RT-PCR amplification of three newly identified reference genes, CGI-119, CTBP1 and GOLGAl, alongside three well-known housekeeping genes, B2M, GAPD, and TUBB, confirmed that the newly identified genes were more stably expressed in individual samples with similar ranges. These results collectively suggest that statistical analysis of microarray data can be used to identify new candidate housekeeping genes showing consistent expression across tissues and diseases. Our analysis identified three novel candidate housekeeping genes (CGI-119, GOLGA1, and CTBP1) that could prove useful for normalization across a variety of RNA-based techniques.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.28 no.3
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• pp.41-51
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• 2020

This research is accomplished to verify objectively how flight instructor's transformative leadership affects student pilot's psychological stabilities and learning satisfactions. Flight instructor's transformative leadership factor divided into individual consideration, intellectual stimulus and charisma from exploring factor analysis. Psychological stability factor subdivided into happiness, concentration and satisfaction. Learning satisfaction factor subdivided into participation, recommendation, persistence, accomplishment and relationship. According to the analysis of flight instructor's transformative leadership effect on psychological stability, it showed that it has statistical significance on happiness, concentration and satisfaction. It also has positive influence on happiness and concentration. The result from regression analysis showed that individual consideration and charisma affected happiness and concentration in order. However, satisfaction from individual consideration, intellectual stimulus and charisma didn't show statistical significance to student pilot's satisfaction. Analysis of flight instructor's transformative leadership on student pilot's learning satisfaction showed statistical significance between them. Intellectual stimulus and charisma had positive influence on student pilot's learning satisfaction. Regression analysis showed charisma and intellectual affect student pilot's learning satisfaction in order.

• Journal of Power System Engineering
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• v.21 no.3
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• pp.37-44
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• 2017

In this study, failure analysis of the angular pin for molding machines to aluminum component molding was carried out. SM45C steel was used for the angular pin, it was surface hardened by the induction surface hardening heat treatment. The cross section of damaged angular pin was observed, and micro Vickers hardness value from the fractured part was measured. Brittle fracture was occurred from the fracture surface of angular pin, therefore, impact toughness value was evaluated by V-notch Charpy impact test. It was confirmed that the impact absorption energy was high when was tempered at a high temperature for a long time, and the toughness was slightly increased. Also, 2-parameter Weibull statistical analysis was investigated in order to evaluate the reliability of the measured micro Vickers hardness values and absorbed energy. The micro Vickers hardness and absorbed energy well followed a two-parameter Weibull probability distribution, respectively. The reverse design against angular pin was proposed as possible by using test results.

• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.14-20
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• 1997

Pipeline network analysis requires fluid mechanics. A lot of equations have been used for flow analysis according to the behavior of fluid in pipelines and the operative situations. In this paper, simulation and analysis have been performed for the pipeline network system in Kyungin area using a steady-state mathematical model. Then, a statistical model using partial least squares(PLS) method has been developed with the data obtained from the developed mathematical model. The results showed that it is possible to simulate and analyze pipeline network systems using statistical approach.

• Journal of Multimedia Information System
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• v.5 no.3
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• pp.195-200
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• 2018

Recently, as the sensor and big data analysis technology have been developed, there have been a lot of researches that analyze the purchase-related data such as the trajectory information and the stay time. Such purchase-related data is usefully used for the purchase pattern prediction and the purchase time prediction. Because it is difficult to find periodic patterns in large-scale human data, it is necessary to look at actual data sets, find various feature patterns, and then apply a machine learning algorithm appropriate to the pattern and purpose. Although existing papers have been used to analyze data using various machine learning methods, there is a lack of statistical analysis such as finding feature patterns before applying the machine learning algorithm. Therefore, we analyze the purchasing data of Songjeong Maeil Market, which is a data gathering place, and finds some characteristic patterns through statistical data analysis. Based on the results of 1, we derive meaningful conclusions by applying the machine learning algorithm and present future research directions. Through the data analysis, it was confirmed that the number of visits was different according to the regional characteristics around Songjeong Maeil Market, and the distribution of time spent by consumers could be grasped.