• Journal of Korean Home Economics Education Association
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• v.17 no.3 s.37
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• pp.109-121
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• 2005

The Purpose of this research was to find out differences in motivation and achievement by gender between traditional group and ARCS group. The subjects were 217 first graders in six different classes from a middle school in Daegu. Each Class had Five lessons during 5 weeks. The collected data were analyzed using descriptive statistics, ANOVA and ANCOVA by SPSS 10.0 program. The results of this study are as follows : First, the ARCS group showed significantly higher score than the control group in motivation. But no significant difference was found between boys and girls and in interactive effects. Statistically significant differences were found in three factors of motivation - attention, relevance and satisfaction - from the ARCS model, but no significant difference in confidence. Second, there was no significant difference in students' achievement.

• Journal of the Korean Statistical Society
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• v.6 no.1
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• pp.47-63
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• 1977

인구가 성장한다는 사실과 함께 인구가 지역적으로 이동하는 현상은 사회과학에 있어서 중요한 연구대상이 되어왔다. 전통적으로 인구의 동학적 측면은 사회학의 주된 연구대상이 되어왔으며 근래에는 인구학(demography)이라는 새로운 독립과학의 분과로서 더욱 이 분야에서의 연구의 심도와 폭이 확대되었다. 그러나 인구의 동학적 측면을 인간의 경제형태의 일환으로 관찰할 수 있는 기회와 가능성이 점차적으로 크게 부각되자 전통적인 경제이론을 적용함으로써 이러한 문제를 분석/구명하고자 하는 동향이 나타나게 되었다. 특히 인구의 단기적 지역간 이동이 경제사회의 질적인 변화와 이에 따르는 산업구조간의 노동력 균형의 재조정에 따라 원인되고 있다는 사실은 인구의 동학적 측면에 경제학적 분석이 당연히 이루어져야 한다는 요구를 초래시키기에 이르렀다.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.95-97
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• 2001

공간자료의 예측문제에 있어 전통적 예측방법인 크리깅방법과 최근 통계적문제 적용되기 시작한 신경망분석방법 간의 비교를 사례연구를 통해 행하였다. 일반적으로 크리깅에 의한 선형예측은 공간자료에 대한 일반적 통계모형으로서 간주되어 왔다. 한편 예측문제에 있어 뉴럴네트워크에 기초한 비모수적 방법이 관심의 대상이 되고 있으며 특히 대용량 자료의 경우 데이터마이닝 기법의 한 분야로 널리 사용되고 있는 실정이다. 본 연구에서는 공간 자료의 예측에 있어 유전자 알고리즘을 신경망분석 모형을 결합하여 기존의 크리깅방법과의 예측력을 비교한다.

• The Korean Journal of Applied Statistics
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• v.8 no.1
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• pp.51-60
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• 1995

Given an $n \times p$ data matrix, if we add the $p_s$ variables somewhat different nature than the p variables to this matrix, we have a new $n \times (p+p_s)$ data matrix. Because of these $p_s$ variables, the traditional principal component analysis can't provide its efficient results. In this study, to improve this problem we review the supplementary principal component analysis putting $p_s$ variables to supplementary variable. This technique is based on the algebraic and geometric aspects of the traditional principal component analysis. So we provide a type of statistical data analysis for the records of eight teams and fourteen fields of the 1982-1992 Korean Pro Baseball Data based on the supplementary principal component analysis and the traditional principal component analysis. And we compare the their results.

• Journal of the Korean association of regional geographers
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• v.7 no.3
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• pp.107-120
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• 2001

This study is concerned with providing a reliable procedure of calibrating a set of places specific distance parameters and with applying it to U.S. inter-State migration flows between 1985 and 1900. It attempts to conform to recent advances in quantitative geography that are characterized by an integration of ESDA(exploratory spatial data analysis) and local statistics. ESDA aims to detect the spatial clustering and heterogeneity by visualizing and exploring spatial patterns. A local statistic is defined as a statistically processed value given to each location as opposed to a global statistic that only captures an average trend across a whole study region. Whereas a global distance parameter estimates an averaged level of the friction of distance, place-specific distance parameters calibrate spatially varying effects of distance. It is presented that a poisson regression with an adequately specified design matrix yields a set of either origin-or destination-specific distance parameters. A case study demonstrates that the proposed model is a reliable device of measuring a spatial dimension of migration, and that place-specific distance parameters are spatially heterogeneous as well as spatially clustered.

• The Journal of Korean Association of Computer Education
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• v.9 no.5
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• pp.1-11
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• 2006

This study aims at identifying what effect ARCS model based instruction and cognitive style have on learning motivation and achievement in computer education. In order to accomplish this goal. 70 high school students were randomly allocated into the experiment group and the control group. And each group was divided into field-independent group and field-dependent group. The results were as follows. First, post test of learning motivation among four groups showed that mean differences were statistical significant. Especially 'ARCS model based instruction-field independent group got higher score than 'traditional instruction field dependent' group. Second, post test of learning achievement showed no significant mean difference among four group. The result of the pre and post test difference analysis showed that the change scores of means were significantly different.

• Proceedings of the Conference
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• 2005.12a
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• pp.40-47
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• 2005

카토그램(Cartogram)은 통계데이터를 이용하여 지리공간에 나타나는 현상을 효과적으로 표현하는 방법 중 하나이다. 카토그램이란 넓은 의미에서 모든 통계지도를 포함하지만, 좁은 의미에서 지도의 변형을 통해 통계데이터의 특징을 표현하는 시각화 방법을 의미한다. 이 연구에서는 후자의 의미에서 카토그램이란 용어를 사용하였으며, 우리나라를 대상으로 절대적 공간을 시간거리 접근성에 의한 공간 카토그램으로 재구성하여 지도화하고, 변화된 공간패턴을 분석하고자 하였다. 카토그램은 특정한 목적을 위해 기본도의 형상이나 지역간의 연속성을 강조시켜서 만든 변형된 지도의 일종으로 전통적인 지도와는 매우 다른 이미지를 주게 되지만, 표현하고자 하는 현상의 공간적 구조를 매우 효과적으로 나타낼 수 있다.

• The Korean Journal of Applied Statistics
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• v.17 no.3
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• pp.519-533
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• 2004

In this paper, we suggest quasi-likelihood estimation (QLE) method and its robust version in estimating spatial dependence modelled through variogram used for spatial data modelling. We compare the statistical characteristics of the estimators with other popular least squares estimators of parameters for variogram model by simulation study. The QLE method for estimating spatial dependence has the advantages that it does not need the concept of lags commonly required for least squares estimation methods as well as its statistical superiority. The QLE method also shows the statistical superiority to the other methods for the tested Gaussian and non-Gaussian spatial processes.

• The Journal of Korean Academy of Prosthodontics
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• v.58 no.2
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• pp.86-94
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• 2020

Purpose: The purpose of this study is to compare the volume stability depending on the mixing methods and storage time for the conventional alginate and extended-pour alginate. Materials and methods: An arch-shaped metal model was fabricated, and one conventional alginate and two extended-pour alginates were used to take impressions using different mixing methods (hand and automatic). 120 impressions were taken (40 per each alginate) and stone models were made in accordance with the different storage times (immediate, 2 days, 5 days, and 6 days). The models were scanned with a 3D table scanner and dimensional change was measured by superimposing the scan data. Using SAS 9.4 (SAS Institute Inc., Cary, NC, USA), the general linear model and Tukey's post hoc test was conducted for statistical analysis (P<.001). Results: There was no statistically significant difference in the dimensional accuracy between two mixing methods, and the volume change was minimum when the stone was poured immediately in all groups. Dimensional accuracy showed a statistically significant difference between groups after 2 days of storage, and extended-pour alginate showed higher accuracy after 5 days of storage comparing to conventional one. Large amounts of volume change were showed at 2 - 5 days for conventional alginate and at 5 - 6 days for extended pour alginate. Conclusion: The mixing method of alginate does not affect volume stability. Although extended-pour alginate has better volume stability than conventional alginate for a long time, it is recommended to pour stone as soon as possible.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.39-43
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• 2000

분산 추정 및 신뢰구간 추정의 한 방법으로 널리 쓰이고 있는 붓스트랩 방법을 복합표본에 적용하는 방법에 대해 알아보았다. 복합 표본은 유한 모집단에서 추출되고 추출확률이 다르기 때문에 i.i.d. 표본에 기초하여 개발된 전통적인 붓스트랩 방법을 직접 적용하면 추론의 오류가 발생할 수 있다. 본 연구에서는 복원 확률비례표본과 랜덤그룹표본에 붓스트랩을 적용하는 방법을 알아보았다.