• Communications for Statistical Applications and Methods
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• v.11 no.3
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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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• v.28 no.4
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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.

• Journal of Families and Better Life
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• v.25 no.3 s.87
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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.

• Korean Journal of Health Education and Promotion
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• v.24 no.5
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• pp.1-21
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• 2007

Adolescent psychosocial smoking prevention programs have been successful, but limited in the magnitude of program effects. The present study is the secondary analysis after the previous study estimated mean effect sizes in smoking knowledge, attitudes, skills, and behaviors with treatment variables. Regardless of overall program effect estimations that other meta.analysis studies have done, this study is conducted to identify explanatory variables that are likely to increase program effects. A decrease of adolescent smoking behaviors is associated with the following factors: a. Younger students ($5^{th}-7^{th}$) than older students ($8^{th}-12^{th}$). b. Research methodology using true experimental design, quasi experimental design with equivalence between groups, use of random assignment, 10% or less attrition rate, use of a no treatment control group, high implementation fidelity, and/or acceptable instrumentation reliability. c. Programs using trained peer leaders, targeting cigarette smoking only, implementing 10 or more treatment sessions and/ or providing booster sessions.

• Korean Journal of Remote Sensing
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• v.33 no.1
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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.

• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.195-204
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• 2007

In order to represent multiple regression data, an alternative graphical method, called as SSR Plot, is proposed by using geometrical description methods. This plot uses the relation that the sum of sqaures for regression (SSR) of two explanatory variables is known as the sum of the SSR of one variable and the increase in the SSR due to the addition of other variable to the model that already contains a variable. This half circle shaped SSR plot contains vectors corresponding explanatory variables. We might conclude that some explanatory variables corresponding to vectors which locate near the horisontal axis do affect the response variable. Also, for the regression model with two explanatory variables, a magnitude of the angle between two vectors can be identified for suppression.

• Journal of Digital Convergence
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• v.7 no.3
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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.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.451-459
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• 1998

The analysis of data from a multiresponse experiment requires careful consideration of the multivariate nature of the data. In a multiresponse sitation, the optimization problem is more complex than in the single response case. The biplot is a graphical tool which make the analyst to understand the correlation of the response variables, the relation of the response variables arid the explanatory variables and the relative importance of the explanatory variables. In case of good fitting of the first order model, we can draw the biplot with the first order experimental design. Otherwise, we can make the biplot with the second order experimental design by adding other experimental points.

• Journal of the Korean Home Economics Association
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• v.32 no.5
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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%.

• Journal of Korean Society for Atmospheric Environment
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• v.24 no.5
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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.