• Journal of The Korean Association For Science Education
• /
• v.17 no.1
• /
• pp.1-10
• /
• 1997

The purpose of this study was to analyze the structural model of causal effects of students' variables on science process skills. Student characteristics investigated in the study included attitude related to the science, logical thinking ability, scientific experiences, cognitive style. Covariance structural modeling procedures were used to test causal inferences about hypothesized relationships. The sample consisted of 319 6th grade students and 321 8th grade students in Seoul City, Korea. Five instruments were used in the study, TSPS(test of science process skills), GALT(group assessment of logical thinking), CEFT(children embedded figures test), questionnaire of attitude related to the science, questionnaire of scientific experience. For statistical analysis, the study adopted the structural equation modeling with LlSREL, a computer statistical program developed by J reskog and S rbom. Major findings of the study are as follows:1) Logical thinking ability has a most strong direct effect on science process skills. 2) The structural coefficient of scientific experience influence on attitude related to the science has the greatest direct one than the others in the covariance structural model. According to the results of this study, it is very importance that various scientific experiences, particularly hands-on activity, should be offer to students to improve science process skills. Also, understanding the relationships of student variable to science process skills will be helpful to decision making on the part of curriculum developers, science teachers and researchers.

• Journal of Family Resource Management and Policy Review
• /
• v.13 no.4
• /
• pp.141-169
• /
• 2009

This study investigated the effect of the money resource on the resource use behaviors of undergraduate students. The aim of the study was the examination of the relationship between undergraduate income and clothes-purchasing behavior. A total of 415 undergraduates residing in Gwangju City and Chonnam Province were interviewed by means of a questionnaire. SPSS 12.0 software was used for statistical analysis. Cronbach's $\alpha$, t-test, one-way ANOVA, Duncan's multiple range analysis and chi-square analysis were all conducted to acquire the results. The main results of this study are as follows. First, the average undergraduate income average was relatively high. Second, the motive for clothing purchases by undergraduates was to buy clothes similar to those which they already had. The undergraduates acquired their purchasing information from past experiences of buying clothes and considered design, quality and color when choosing their clothing. They purchased their clothes at retail stores and on average bought clothes about once a month. Although theaverage monthly expenditure on clothes was over 100,000 won, in general, the undergraduates spent between 50,000 and 100,000 won a month. They mainly paid cash when they purchased their clothes. Third, by analyzing the income, clothes-purchasing behavior, and clothes purchasing frequency of undergraduates, significant statistical differences in average monthly clothes expenditure and payment methods were detected. The results of the study can be used to understand the patterns of undergraduate money resource usage behaviors and can be utilized as an educational resource at home and within educational institutions.

• Proceedings of the KSME Conference
• /
• 2008.11a
• /
• pp.1314-1319
• /
• 2008

Fatigue reliability problems are nowadays actively considered in the design of mechanical components. Recently, Dimension Reduction Method using Kriging approximation (KDRM) was proposed by the authors to efficiently calculate statistical moments of the response function. This method, which is more tractable for its sensitivity-free nature and providing the response PDF in a few number of analyses, is adopted in this study for the reliability analysis. Before applying this method to the practical fatigue problems, accuracies are studied in terms of parameters of the KDRM through a number of numerical examples, from which best set of parameters are suggested. In the fatigue reliability problems, good number of experimental data are necessary to get the statistical distribution of the S-N parameters. The information, however, are not always available due to the limited expense and time. In this case, a family of curves with prediction interval, called P-S-N curve, is constructed from regression analysis. Using the KDRM, once a set of responses are available at the sample points at the mean, all the reliability analyses for each P-S-N curve can be efficiently studied without additional response evaluations. The method is applied to a spring design problem as an illustration of practical applications, in which reliability-based design optimization (RBDO) is conducted by employing stochastic response surface method which includes probabilistic constraints in itself. Resulting information is of great practical value and will be very helpful for making trade-off decision during the fatigue design.

• The KIPS Transactions:PartD
• /
• v.12D no.4 s.100
• /
• pp.657-662
• /
• 2005

The Non-Sampling Error can happen any time by means of the intended or unintended error by the interviewer or respondent, but it is very difficult to find the error in survey database because it can hardly be computed mathematically and systematically. Until now, we have found it accidentally through the simple relation between the items or through the inspection from the random field. Therefore we introduced an heuristic methodology that can detect the interviewer's error by statistical decision-making or data mining techniques with a case study. It will be helpful so as to improve the statistical duality and provide efficient field management for the supervisor.

• Journal of the Korea Institute of Building Construction
• /
• v.9 no.6
• /
• pp.131-139
• /
• 2009

It is critical to select the appropriate type of tower cranes for the construction of super tall buildings. However the selection is often made based on subjective personal experiences due to the lack of historical and analytical data. As a result, planning mistakes and efficiency errors sometimes occur. This research is to develop a system of hoisting analysis for appropriate tower crane selection and to provide a flexible statistical model based on the Burj Dubai project. In addition, this system hassupporting functions that can estimate the target construction period per floor, and a decision-making construction period computation method which is based on the characteristic of the selected tower cranes.

• Journal of the Korean Society for Marine Environment & Energy
• /
• v.18 no.3
• /
• pp.233-244
• /
• 2015

Marine ecosystem modelling has become a more widely used decision-making tool in coastal ecosystem-based management. However, it is not trivial to develop a well calibrated/validated model with potential applicability and practicality because understanding ecological processes with complexities is a pre-requisite for developing robust ecosystem models and this accompanies a great deal of well coordinated efforts among field-going ecologists, laboratory scientists, modelers, stake-holders and managers. This report aims to provide a brief introduction on two different approaches in marine ecological models: deterministic (mechanistic) and stochastic (statistical) approach. We also included definitions, historical overview of past researches, case studies, and contextual suggestions for coastal management in Korea. A long list of references this report included in this study might be used as an introductory material for those who wish to enter ecosystem modelling field.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.8 no.6
• /
• pp.483-490
• /
• 2015

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. It is shown that it is possible to do likelihood inference for software reliability models based on finite failure model and non-homogeneous Poisson Processes (NHPP). For someone making a decision about when to market software, the conditional failure rate is an important variables. The infinite failure model are used in a wide variety of practical situations. Their use in characterization problems, detection of outlier, linear estimation, study of system reliability, life-testing, survival analysis, data compression and many other fields can be seen from the many study. Statistical process control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper, proposed a control mechanism based on NHPP using mean value function of Musa-Okumo and Power law type property.

• Journal of the Korea Society for Simulation
• /
• v.20 no.4
• /
• pp.115-125
• /
• 2011

Manufacturing companies usually manage the process to achieve high quality using various types of control chart in statistical process control. When an assignable cause occurs in a process, the data in the control chart changes with different patterns by the specific causes. It is important in process control to classify the CCP (Control Chart Pattern) recognition for fast decision making. In former research, gathered data from process used to apply as raw data, leads to degrade the performance of recognizer and to decrease the learning speed. Therefore, feature based recognizer, employing feature extraction method, has been studied to enhance the classification accuracy and to reduce the dimension of data. We propose the method to extract features that take the distances between CCP data and reference vector generated from BDK (Bi-Directional Kohonen Network). We utilize those features as the input vectors in ANN (Artificial Neural Network) and compare with raw data applied ANN to evaluate the performance.

• Journal of the Korean Society for Library and Information Science
• /
• v.51 no.1
• /
• pp.245-269
• /
• 2017

To assess and understand the level of job burnout among the medical librarians and identify the factors affecting it, email survey, consisting of questions regarding personal background, organization characteristics, degree of burnout, and coping activities, was administered for two weeks in December 2016, to the librarians in 138 institutions affiliated with the Korean Medical Library Association. MBI-GS is used as a burnout measuring instrument. Data collected from 158 librarians (response rate 65.6%), were analyzed by descriptive statistics, t-test, ANOVA, and correlation analysis methods. According to MB-GS norm, respondents experience moderate level of exhaustion and professional efficacy, however the level of cynicism is extremely high. There are significant statistical differences in burnout, depending on age, medical librarian certificate, work experience, type of library, job coverage, and coping activities. There are also statistical significant correlations between workload, role conflict, decision making process and performance evaluation, organization communication, IT environment, continuing education and burnout. Based on the results, recommendations to alleviate burnout are suggested in individual, organizational, and professional association context.

• The Korean Journal of Applied Statistics
• /
• v.33 no.3
• /
• pp.321-334
• /
• 2020

The US Federal Reserve (Fed) has decided to cut interest rates. When we look at the expression of the FOMC statements at the time of policy change period we can understand that Fed has been communicating with markets through a change of word selection. However, there is a criticism that the method of analyzing the expression of the decision sentence through the context can be subjective and limited in qualitative analysis. In this paper, we evaluate the signaling effect of FOMC statements based on previous research. We analyze decision making characteristics from the viewpoint of text mining and try to predict future policy trend changes by capturing changes in expressions between statements. For this purpose, a decision tree and neural network models are used. As a result of the analysis, it can be judged that the discrepancy indicators between statements could be used to predict the policy change in the future and that the US Federal Reserve has systematically implemented policy signaling through the policy statements.