• 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 the Korean Society of Safety
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• v.33 no.4
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• pp.98-104
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• 2018

Macroscopic accident analyses have been conducted to incorporate transportation safety into long-term transportation planning. In macro-level accident prediction model, exposure variable(e.g. a settled population) have been used as fundamental explanatory variable under the concept that each trip will be subjected to a probable risk of accident. However, a settled population may be embedded error by exclusion of active population concept. The objective of this research study is to develop macro-level accident prediction model using floating population variable(concept of including a settled population and active population) collected from mobile phone data. The concept of accident prediction models is introduced utilizing exposure variable as explanatory variable in a generalized linear regression with assumption of a negative binomial error structure. The goodness of fit of model using floating population variable is compared with that of the each models using population and the number of household variables. Also, log transformation models are additionally developed to improve the goodness of fit. The results show that the log transformation model using floating population variable is useful for capturing the relationships between accident and exposure variable and generally perform better than the models using other existing exposure variables. The developed model using floating population variable can be used to guide transportation safety policy decision makers to allocate resources more efficiently for the regions(or zones) with higher risk and improve urban transportation safety in transportation planning step.

• Journal of Korean Academy of Nursing
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• v.34 no.5
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• pp.673-684
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• 2004

Purpose: The purpose of the study was to meta-analyze the relationships of major concepts, which were made by synthesizing similar explanatory variables into more comprehensive concepts, to hope. Method: The relevant researches from Jan 1980 to Dec 2003, performed in adults or adult patients, were collected. Using the SAS program, meta-analysis were done with the input data of the number of subjects, the correlation coefficients provided from most of the studies or a few transformed correlation coefficients from F value. In order to get the analysis to be done in homogeneous status of the data regarding each relationship of each major concept to hope(p> 0.05), heterogeneous data were eliminated in repeating Q-test. Result: The major variable regarding relationship to self/transcendental being/life(spiritual wellbeing & self esteem) and social support(social support & family support) have very large positive effects on hope(D=l.72, D=l.27). The negative effect of the variable regarding captive state(uncertainty in illness, perceived unhealthiness status, & fatigue) and positive effect of coping(approach coping) on hope are in the level between moderate to large(D=-0.61, D=0.78). All the effects of the major concepts on hope were verified as significant statistically(p=.000). The Fail -Safe numbers showed the significant effects of the three major concepts except coping on hope were reliable. Conclusion: The results can be a guide to advance hope theory for nursing.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1153-1164
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• 2008

The ozone data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model have been considered for analyzing the ozone data at the northern part of the Gyeonggi-Do, Uijeongbu monitoring site in Korea. The result showed that both overall and monthly ARE models are suited for describing the ozone concentration. In the ARE model, seven meteorological variables and four pollution variables are used as the as the explanatory variables for the ozone data set. The seven meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, dew point temperature, steam pressure, and amount of cloud. The four air pollution explanatory variables are Sulfur dioxide(SO2), Nitrogen dioxide(NO2), Cobalt(CO), and Promethium 10(PM10). Also, the high level ozone data (over 80ppb) have been analyzed four ARE models, General ARE, HL ARE, PM10 add ARE, Temperature add ARE model. The result shows that the General ARE, HL ARE, and PM10 add ARE models are suited for describing the high level of ozone data.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1117-1124
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• 2010

The PM10 (Promethium 10) data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model has been considered for analyzing the monthly PM10 data at the southern part of the Gyeonggi-Do, Suwon monitoring site in Korea. In the ARE model, six meteorological variables and four pollution variables are used as the explanatory variables for the PM10 data set. The six meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, radiation, and amount of cloud. The four air pollution explanatory variables are sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), carbon monoxide (CO), and ozone ($O_3$). The result showed that the monthly ARE models explained about 13-49% for describing the PM10 concentration.

• Physical Therapy Rehabilitation Science
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• v.11 no.2
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• pp.181-188
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• 2022

Objective: The purpose of this study to investigate the correlations among the motor function, balance, and gait velocity and the strength that could explain the variation of gait velocity of chronic stroke survivors. Design: This was a cross-sectional cohort study. Methods: Thirty hemiplegic stroke survivors hospitalized in an inpatient rehabilitation center were participated. The muscle tone of ankle plantarflexor and muscle strength of ankle dorsiflexor were measured respectively with modified Ashworth scale (MAS) and hand-held dynamometer. And the motor recovery and function with Fugl-Meyer assessment (FMA), balance with Berg balance scale (BBS) and timed up and go (TUG) test were measured. Gait velocity was measured with GAITRite. The correlation among motor function, muscle tone, muscle strength, balance, and gait were analyzed. In addition, the strength of the relationship between the response (gait velocity) and the explanatory variables was analyzed. Results: The gait velocity had positive correlations with FMA, muscle strength, and BBS, and negative correlation with MAS and TUG. Regression analysis showed that TUG (𝛽=-0.829) was a major explanatory variable for gait velocity. Conclusions: Our results suggest that gait velocity had correlations with muscle strength, MAS, FMA, BBS, and TUG. The tests and measurements affecting the variation of gait velocity the greatest were TUG, followed by FMA, BBS, muscle strength, and MAS. This study shows that TUG would be a possible assessment tool to determine the variation of gait velocity in stroke rehabilitation.

• Journal of the Korean Statistical Society
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• v.12 no.2
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• pp.81-90
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• 1983

The least squares estimator of disturbance variance in a regression model is biased under a serial correlation. Under the assumption of an AR(I), Theil(1971) crudely related the bias with the autocorrelation of the disturbances and the autocorrelation of the explanatory variable for a simple regression. In this paper we derive a relation which relates the bias with the autocorrelation of disturbances and the autocorrelation of explanatory variables for a multiple regression with improved precision.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.77-91
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• 2002

This paper considers a functional regression model with truncated errors in explanatory variables. We show that the ordinary least squares (OLS) estimators produce bias in regression parameter estimates under misspecified models with ignored errors in the explanatory variable measurements, and then propose methods for analyzing the functional model. Fully parametric frequentist approaches for analyzing the model are intractable and thus Bayesian methods are pursued using a Markov chain Monte Carlo (MCMC) sampling based approach. Necessary theories involved in modeling and computation are provided. Finally, a simulation study is given to illustrate and examine the proposed methods.

• Journal of Korean Society of Forest Science
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• v.95 no.5
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• pp.539-542
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• 2006

This study confirmed econometrically the causality of forest inventory and roundwood supply using Korean data. In general, forest inventory is included as explanatory variable in roundwood supply function. We checked whether each series is stationary or not before using it in the model, and determined whether the combination of the series is comtegrated. The relationship between forest inventory and roundwood supply was represented by bivariate vector autoregressive model. The causality of forest evidence of the causal relationship between change in forest inventory and change in roundwood supply in Korea. That is, change in forest inventory does not cause change in roundwood supply in Korea. It seems reasonable not to include forest inventory as explanatory variable in roundwood supply function in Korea.

• The Journal of Fisheries Business Administration
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• v.26 no.2
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• pp.75-92
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• 1995

This paper tried to estimate the pass - through of exchange rate changes to export price of fishery products using export price function. The results are as follows : 1) The variable of fluctuation of exchange rate of Won(equation omitted) to Yen(equation omitted)(variable E2) is more powerful explanatory variable than that of Won to U.S. dollar to explain the fluctiation of export price of fishery products(varible $P_{t}$)- 2) The variable of fish catches(variable K $P_{t}$) is also found to be a statistically significant varible but that of producer price index is not found. 3) The variable E2 have statistically a more influence on variable $P_{t}$ than variable K $P_{t.}$ 4) The estimation shows us that 1% of fluctuation of variable E2 could result in 0.9978% of fluctuation of variable $P_{t.}$