• Communications for Statistical Applications and Methods
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• 제11권3호
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• pp.529-537
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• 2004

This article concerns dynamic graphical methods for visualizing a curvature in regression problem in which some predictors enter nonlinearly. A sequence of augmented partial residual plot or partial residual plot updated by the change of linear combination of two predictors are constructed. Examples demonstrate that the suggested methods can be used to reduce the dimension of explanatory variables as well as to capture a curvature.

• ETRI Journal
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• 제28권4호
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• pp.541-544
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• 2006

From its foundation until 2004, ETRI has registered over 1,000 US patents. This letter analyzes the characteristics of these patents and addresses the explanatory factors affecting their citation counts. For explanatory variables, research team related variables, invention specific variables, and geographical domain related variables are suggested. Zero-altered count data models are used to test the impact of independent variables. A key finding is that technological cumulativeness, the scale of invention, outputs in the electronic field, and the degree of dependence on the US technology domain positively affect the citation counts of ETRI-invented US patents. The magnitude of international presence appears to negatively affect the citation counts of ETRI-invented US patents.

• 가정과삶의질연구
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• 제25권3호
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• pp.77-87
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• 2007

This study purports to understand the direct and indirect effects between eating disorder behavior of female adolescents and their body-related variables(concerning the degree of diet regime, weight control, body satisfaction, and obesity), self-esteem and internal control, by checking three sub-categorized behavior of eating disorders of diet behavior, bulimia behavior, and eating control behavior. The sample group used for the study consisted of 190 female high school students and 292 female university students; measurement devices used for the study were those of body-related variables, self esteem and internal control, and eating disorder behavior; and data analysis was performed using ${\chi}2$, t-test, Pearson's correlation, regression analysis and path analysis. The results are as follows. First, there is a significant difference between university students and high school students regarding their body satisfaction, weight control experience, and self esteem. University students are more satisfied with their body, have higher self esteem, and control their weight better than high school students. Second, diet behavior shows a correlation with the degree of diet interest, weight control experience, and body satisfaction. Body satisfaction and internal control proved to be correlated with bulimia behavior, while weight control experience, obesity, and self esteem were correlated with eating control behavior. Third, the variables that showed a direct influence on diet behavior as an eating disorder are diet interest, weight control experience, body satisfaction and obesity, in that the explanatory power of the variables is 60.7% with the highest mark on obesity. The variables that showed effects on bulimia are body satisfaction and internal control with an explanatory power of 2.8%. Indirect variables effecting bulimia include objects, diet interest, body satisfaction, and self esteem. The variable with a direct influence on eating control behavior was self esteem with and explanatory power of 4%, whereas the variables of objects, diet interest, body satisfaction, weight control experience, and internal control were all indirectly correlated with eating control behavior.

• 보건교육건강증진학회지
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• 제24권5호
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• pp.1-21
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• 2007

청소년을 위한 학교 흡연예방 프로그램은 사회심리 이론에 근거한 프로그램이 대체로 성공적이었다고 알려져 있으나, 각 프로그램의 효과 정도에는 많은 차이가 있다. 이 연구는 다른 메타 분석처럼 전체적인 프로그램 효과도를 측정하여 일반적인 결론을 유도한 것이 아니라, 프로그램의 효과와 관계가 깊은 요인 (Explanatory Variables)을 자세히 파악하여 보건교육 담당자, 연구원, 또는 정책 결정자들에게 구체적인 가이드라인을 제공하는 데에 목적을 두고 있다. 주요한 연구결과는 다음과 같다. 1. 8-12학년 학생들보다는 초등학교에서 중등학교로 바뀌는 5-7학년 학생들에게 흡연예방 프로그램은 더 효과가 있었다. 2. 연구 방법론에 있어서는 experimental design, random assignment, 순수 비교그룹을 사용하였을 경우, implementation fidelity와 instrument reliability가 높은 경우, 또는 10% 미만의 attrition rates일 때 프로그램 효과도 (effect size)가 더 높게 나타났다. 3. 프로그램 실행 시 또래 리더를 사용하였을 경우, 알코올 등 다른 약물을 배제한 담배만을 중점적으로 다루었을 경우, 적어도 10회 이상 연속적으로 이루어지거나 프로그램 종료 후 일년 뒤에 추가 프로그램이 주어진 경우가 더욱 효과적이었다.

• 대한원격탐사학회지
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• 제33권1호
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• 응용통계연구
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• 제20권1호
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• pp.195-204
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• 2007

기하학적인 방법을 사용하여 다중회귀모형 자료를 그래프로 구현하는 회귀제곱합 그림을 제안한다. 두 설명변수의 회귀제곱합은 한 변수의 단순회귀제곱합과 한 변수의 회귀모형에 다른 변수가 추가되었을 때 회귀제곱합의 증가분의 합으로 표현되는 관계식을 이용하여 회귀제곱합 그림을 반원의 형태로 구현한다. 회귀제곱합 그림은 설명변수에 대응하는 벡터로 표현되고, 반응변수에 영향력 정도를 시각적으로 구현하는 그래픽적인 방법이다. 수평축에 가까운 벡터에 대응하는 설명변수가 반응변수에 더 많은 영향을 주는 설명변수라고 판단할 수 있다 또한 두개의 설명변수에 대응하는 벡터 사이의 각도 크기로 서프레션의 발생여부를 진단 가능하다.

• 디지털융복합연구
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• 제7권3호
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• pp.13-24
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• 2009

Technology Acceptance Model (TAM) has been a basis model for testing technology use. Post researches of TAM have been conducted with the updating the TAM by adding new independent variables in order to increase the explanatory power of the model. However, the problem is that different independent variables have to be required to keep the explanatory power whenever adopting particular technology. This might reduce the generality of the research model. Thus in order to increase the generality of the model, this study reviewed the previous researches and collected the independent variables used, and regrouped them into three basic independent constructs. New research model was designed with three basic independent constructs with three constructs selected for the involuntary information technology usage environment. Finally, this study concluded that new technology acceptance model could be used to explain the use of new technology without any adding new particular independent variables.

• 응용통계연구
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• 제11권2호
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• pp.451-459
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• 1998

반응표면분석에서 다반응값의 최적화 문제는 단반응값 최적화문제보다 복잡하다. 이런 다반응값 문제에서 반응변수들이나 설명변수 상호간의 관계나 중요성 등을 평가하는 것은 중요하다. 이러한 평가를 위하여 biplot를 이용할 수 있는데, 1차 회귀모형이 적합치 않은 경 우, 2차 회귀모형을 위한 순차적 실험계획을 이용하여 2차 회귀 모형에 대응되는 biplot를 그려 선형 및 비선형효과를 알 수 없게 된다.

• 대한가정학회지
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• 제32권5호
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• pp.153-164
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• 1994

The purpose of this study was to investigate the relationship between appearance satisfaction clothing satisfaction body cathexis sociality and achievement motive and to examine how sociality and achievement motive was influenced by socio-economic level grade appearance satisfaction clothing satisfaction and body and body cathexis of middle school boys and girls. the subjects were 297 middle school students in Seoul :139 were boys and 158 girls. The results of the study were as follows: 1. There were significant positive relationships between appearance satisfaction clothing satisfaction body cathexis sociality and achievement motive in boys and girls. 2. Appearance satisfaction body cathexis and achievement motive were significantly higher in body than in girls. Sociality was significantly higher in girls than in boys. 3. In boys appearance satisfaction was influenced by socio-economic level grade, and clothing satisfaction, and the explanatory power by the one variable was 25.8% 4. Sociality was influenced by appearance satisfaction clothing satisfaction and socio-economic lecel in boys and girls. The explanatory powers by the 3 variables were 27.1% in boys and 16.5% in girls. Achievement motive was influenced by grade and appearance satisfaction in boys. The explanatory power by the 2 variables was 13.2% In girls achievement motive was influenced by clothing satisfaction and the explanatory power by the one variable was 10.4%.

• 한국대기환경학회지
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• 제24권5호
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• pp.604-612
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• 2008

The ozone concentration is one of the important environmental issue for measurement of the atmospheric condition of the country. This study focuses on applying the Autoregressive Error (ARE) model for analyzing the ozone data at middle part of the Gyeonggi-Do, Anyang monitoring site in Korea. In the ARE model, eight meteorological variables and four pollution variables are used as the explanatory variables. The eight meteorological variables are daily maximum temperature, wind speed, amount of cloud, global radiation, relative humidity, rainfall, dew point temperature, and water vapor pressure. The four air pollution variables are sulfur dioxide $(SO_2)$, nitrogen dioxide $(NO_2)$, carbon monoxide (CO), and particulate matter 10 (PM10). The result shows that ARE models both overall and monthly data are suited for describing the oBone concentration. In the ARE model for overall ozone data, ozone concentration can be explained about 71% to by the PM10, global radiation and wind speed. Also the four types of ARE models for high level of ozone data (over 80 ppb) have been analyzed. In the best ARE model for high level of ozone data, ozone can be explained about 96% by the PM10, daliy maximum temperature, and cloud amount.