• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.2 s.150
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• pp.266-274
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• 2006

This study aims to examine the effect of mass media on fashion leadership of make-up and fashion products. The subjects of the study were 168 women consumers who lived in Seoul. Data were collected during September in 2003. Statistical analyses used in this study were frequency, Cronbach's $\alpha$, ANOVA, Duncan test, t-test, and multiple regression. The results of this study were as follows; 1. As a result of multiple regression for the effect of mass media on fashion leadership of make-up, there was a significant effect of movies on fashion leadership of make-up. 2. As a result of multiple regression for the effect of mass media on fashion leadership of fashion product, there were significant effects of magazines, internet, and movies on fashion leadership of fashion product. 3. In regard to the effects of consumer characteristics on fashion leadership, there were significant effects of consumer characteristics on fashion leadership of make-up and fashion product.

• Journal of Environmental Science International
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• v.19 no.3
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• pp.305-312
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• 2010

The purpose of this study is to compare the relative growth of annual ring width of red pine(Pinus densiflora), black pine(Pinus thunbergii) and pitch pine(Pinus rigida) by means of multiple regression method according to Graybill hypothesis. The obtained results are as follows. 1. The changes of rainfall have affected to tree growth during the periods of 1975 through 1978. 2. Among these pine trees, red pine was mostly influenced by environmental factors. 3. The growth of annual ring width was sensitively responded to the changes of rainfall and air temperature. 4. Among the heavy metals analyzed, the concentrations(ppm) of Lead(Pb) and Copper(Cu) were negatively effected on the growth of annual ring width of pine trees. 5. The analytical technique of annual ring width may be useful for estimation of the pollution in forest areas near industrial complexes.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.2
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• pp.138-145
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• 2016

This paper proposes an improved tabu search method for subset selection in multiple linear regression models. Variable selection is a vital combinatorial optimization problem in multivariate statistics. The selection of the optimal subset of variables is necessary in order to reliably construct a multiple linear regression model. Its applications widely range from machine learning, timeseries prediction, and multi-class classification to noise detection. Since this problem has NP-complete nature, it becomes more difficult to find the optimal solution as the number of variables increases. Two typical metaheuristic methods have been developed to tackle the problem: the tabu search algorithm and hybrid genetic and simulated annealing algorithm. However, these two methods have shortcomings. The tabu search method requires a large amount of computing time, and the hybrid algorithm produces a less accurate solution. To overcome the shortcomings of these methods, we propose an improved tabu search algorithm to reduce moves of the neighborhood and to adopt an effective move search strategy. To evaluate the performance of the proposed method, comparative studies are performed on small literature data sets and on large simulation data sets. Computational results show that the proposed method outperforms two metaheuristic methods in terms of the computing time and solution quality.

• Korean Management Science Review
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• v.32 no.3
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• pp.71-89
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• 2015

This research empirically investigated the influence factors on the activation of environmental innovations (EI) in Korean manufacturing firms. In this study, external factors (compulsory demand, government regulation, normative pressure and imitative pressure) and internal factors (environmental resources, top management support, integration of environmental tasks, capabilities of environmental personnel, and environmental strategy/environmental management systems) were totally considered. The results of a multiple regression analysis showed that influence factors such as top management support, environmental resources and integration of environmental tasks have a significant and positive impact on levels of EI. However, the effects of external factors were not statistically significant. We also examined whether capabilities of environmental personnel as well as environmental resources, which are directly related with degrees of EI, have a moderating impact on relationships between other internal factors and levels of EI. With a subgroup analysis, the moderating role of abilities of environmental personnel were empirically confirmed. Through a multiple regression analysis, the direct effects of external factors on the adoption or construction of internal factors were demonstrated. The effects of government regulation, normative pressure and imitative pressure on internal factors were significant and positive. It was also found that external factors have indirect effects on EI through internal factors. Finally, the results of multiple regression analyses indicated that EI positively influences the achievement of environmental competitive benefits, and environmental competitive advantages can improve the organizational performance of a firm.

• Journal of Korean Society for Atmospheric Environment
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• v.10 no.4
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• pp.224-232
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• 1994

A new neural network model has been developed to predict short-term air pollution concentration. In addition, a multiple regression model widely used in statistical analysis was tested. These models were applied for prediction of daily maximum ozone concentration in Seoul during the summer season of 1991. The time periods between May and September 1989 and 1990 were utilized to train set of learning patterns in neural network model, and to estimate multiple regression model. To evaluate the results of the different models, several Performance indices were used. The results indicated that the multiple regression model tended to underpredict the daily maximum ozone concentration with small r$^{2}$(0.38). Also, large errors were found in this model; 21.1 ppb for RMSE, 0.324 for NMSE, and -0.164 for MRE. On the other hand, the results obtained from the neural network model were very promising. Thus, we can know that this model has a prominent efficiency in the adaptive control for the non-linear multi- variable systems such as photochemical oxidants. Also, when the recent new information was added in the neural network model, prediction accuracy was increased. From the new model, the values of RMSE, NMSE and r$^{2}$ were 13.2ppb, 0.089, 0.003 and 0.55 respectively.

• Journal of Biosystems Engineering
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• v.23 no.6
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• pp.561-570
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• 1998

The MLR(Multiple Linear Regression) models to estimate soluble solids content non-destructively were presented to make a selection of optimal photosensor utilized to measure the soluble solids content of apples. Visible and NIR absorbance in the 400 to 2498 nanometer(nm) wavelength region, soluble solids content(sugar content), hardness, and weight were measured for 400 apples(gala). Spectrophotometer with fiber optic probe was utilized for spectrum measurement and digital refractometer was used for soluble solids content. Correlation between absorbance spectrum and soluble solids content was analyzed to pick out the optimal wavelengths and to develop corresponding prediction model by means of MLR. For the coefficient of determination($R^2$) to be over 0.92, the MLR models out of the original absorbance were built based on 7 wavelengths of 992, 904, 1096, 1032, 880, 824, 1048nm, and the ones of the second derivative absorbance based on 5 wavelengths of 784, 1056, 992, 808, 872nm. The best model of the second derivative absorbance spectrum had $R^2$=0.91, bias= -0.02bx, SEP=0.28bx for unknown samples.

• Journal of the Korean Society of Tobacco Science
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• v.24 no.1
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• pp.13-20
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• 2002

This study was conducted to evaluate the prediction of sensory attributes of tobacco smoke by the use of volatile oil components of tobacco. For analytical and sensory evaluations, twelve aging tobaccos (i.e., 3 crop years; 1998, 1999, 2000, and 4 stalk positions) were prepared in flue-cured and burley tobaccos. 61 volatile oil components and 5 sensory attributes such as irritation, impact, after taste, bitter and green were investigated. Irritation of flue-cured tobacco, and irritation and impact of burley tobacco were significantly increased with the ascending stalk position, whereas after taste of burley tobacco was significantly decreased. Significantly positive correlations among irritation, impact, and bitter were observed in flue-cured tobacco. A significantly positive correlation between irritation and bitter was observed, significantly negative correlations between after taste and irritation and between after taste and impact were observed in burley tobacco. Except for green of burley tobacco, all probabilities of multiple linear regression equations between volatile oil components of tobacco and sensory attributes of tobacco smoke were significant(P$\leq$0.05). This study suggests that the multiple linear regression equations may be useful to predict the sensory attributes of tobacco smoke with a few selected volatile oil components of tobacco.

• Journal of Korea Water Resources Association
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• v.33 no.5
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• pp.635-645
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• 2000

In tidal rivers, the river level, discharge and tide are interrelated. Therefore, the stage-discharge relation that takes no account of tidal effects is inaccurate. For the calculation of river discharge in low water level, this paper attempts to formulate a multiple regression equation of stage-discharge curve to calculate the river discharge in low water level with variables as river level and differences between sea level and river level. Numerical application were perfonned on Ulsan gaging station in Taehwa river, and the comparison with existing rating curve equation showed good applicability of this multiple regression equation.uation.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.1
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• pp.11-20
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• 2017

This study is to estimate the spatial soil moisture using multiple linear regression model (MLRM) and 15 minutes interval Land Surface Temperature (LST) data of Communication, Ocean and Meteorological Satellite (COMS). For the modeling, the input data of COMS LST, Terra MODIS Normalized Difference Vegetation Index (NDVI), daily rainfall and sunshine hour were considered and prepared. Using the observed soil moisture data at 9 stations of Automated Agriculture Observing System (AAOS) from January 2013 to May 2015, the MLRMs were developed by twelve scenarios of input components combination. The model results showed that the correlation between observed and modelled soil moisture increased when using antecedent rainfalls before the soil moisture simulation day. In addition, the correlation increased more when the model coefficients were evaluated by seasonal base. This was from the reverse correlation between MODIS NDVI and soil moisture in spring and autumn season.

• The Journal of the Korea Contents Association
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• v.18 no.6
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• pp.414-423
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• 2018

The purpose of this study was to examine influences on grit in the nursing student. The data were collected from 146 nursing students in the H university located Y city. The data were analyzed by descriptive statistics, T-test, ANOVA, Scheffe, Pearson's correlation coefficient, and multiple regression with SPSS 20.0 program. Grit was found to be in a significant positive correlation with academic stress, academic self-efficacy and major satisfaction. The result of the multiple regression indicates the major satisfaction, academic self-efficacy, academic stress and grade point average predict 22.5% of grit. Therefore, it is necessary to develop an intervention program to strengthen the grit of nursing students.