• Proceedings of the KWS Conference
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• 1999.05a
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• pp.203-206
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• 1999

• Proceedings of the KWS Conference
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• 2001.05a
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• pp.36-39
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• 2001

• Journal of the Korea Society for Simulation
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• v.26 no.3
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• pp.23-34
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• 2017

This paper is to study the simulation model of depot maintenance system that analyzes logistics supportability such as component availability and cost of target equipment. A depot maintenance system could repair or maintain multiple components simultaneously. The key performance indicators of this system are component availability, repair cycle time, and maintenance cost. The simulation model is based on the engine maintenance process of army aviation depot. This study combines the NOLH(Nearly Orthogonal Latin Hypercube) experimental design method, to composes 33 scenarios, with a multiple regression analysis to find out major factors that influence on key performance indicators. This study is significant in providing a cost-effectiveness analysis on depot maintenance system that is capable of maintaining multiple components at the same time.

• Journal of Korea Water Resources Association
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• v.45 no.11
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• pp.1131-1141
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• 2012

In this study, factors that influence water supply capacities of dams are extracted using factor analysis, and multiple regression equations for estimating water supply capacities of dams are developed using the analysis results. Twenty-one multi-purpose dams and twelve Municipal and Industrial (M&I) water supply dams are selected for case studies, and eight variables influencing water supply capacities of dams, namely: watershed area, inflow, effective reservoir storage, grade on amount of M&I water supply, grade on amount of agricultural water supply, grade on amount of in-stream flow supply, grade on river administration, and grade on average rainfall, are determined. Two case studies for multi-purpose dams and M&I water supply dams are performed, employing factor analysis, respectively. For the two cases, preliminary tests, such as reviewing matrix of correlation coefficient, Bartlett's test of sphericity, and Kaiser-Meyer-Olkin (KMO) test, are conducted to evaluate the suitability of the variables for factor analysis. In case of multi-purpose dams, variables are grouped into three factors; M&I water supply dams, two factors. The factors are rotated using Varimax method, and then factor loading of each variable is computed. The results show that the variables influencing water supply capacities of dams are reasonably selected and appropriately grouped into factors. In addition, multiple regression equations for predicting the amounts of annual water supply of dams are established using the factor scores as explanatory variables, it is identified that the models' accuracies are high, and their applications to determining effective storage capacity of a dam during dam planning and design steps are presented. Consequently, it is thought that the variables and factors are useful for dam planning and dam design.

• Journal of the Korean Society of Food Culture
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• v.24 no.6
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• pp.711-720
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• 2009

The purpose of this study was to understand the effects of hotel employees' physical attractiveness on person-job fit and to empirically analyze whether self-esteem and self-efficacy play a mediating role in the causality between an employee's physical attractiveness and person-job fit. Self-administered questionnaires were completed by 345 employees and the data were analyzed by frequency analysis, factor analysis, reliability analysis, correlation analysis and multiple regression analysis. The primary results were as follows: Multiple regression analysis showed that hotel employee physical attractiveness had a positive significant influence on self-esteem ($\beta=.504$, p<.001) and self-efficacy ($\beta=.441$, p<.001). Also, employee selfesteem ($\beta=.281$, p<.001) and self-efficacy ($\beta=.478$, p<.001) each had a positive significant influence on person-job fit. As a result of analyzing the mediating role, the effect of hotel employees' physical attractiveness on person-job fit was partially mediated by self-esteem and self-efficacy.

• Journal of Information Technology Services
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• v.23 no.2
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• pp.1-12
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• 2024

This study aims to find ways to increase the declining donation intention by using machine learning techniques. To this end, in order to predict factors that affect donations between the MZ generation and the senior generation, various machine learning algorithms, including logistic regression analysis, are applied to build a model to determine variables that affect donation intention, and provide statistical verification and evaluation indicators. In this study, differences in donation intention by generation were expected as a variable affecting donation intention, and the senior generation was expected to show a higher donation intention tendency than the younger generation. However, although the research results were not statistically significant, the younger generation showed a higher intention to donate, and these results are interpreted to mean that value consumption and ethical consumption, which are important to today's MZ generation, also influenced donations. However, there were differences between generations in the amount of donations, and higher donation amounts were confirmed among the senior generation (those in their 50s or older) than the younger generation. In addition, the results of the logistic regression analysis showed that previous donation experience had a positive effect on future donation intention, and the more motivation and importance of donation and various social participation activities online and offline, the more active one became in donating.

• KIEAE Journal
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• v.9 no.1
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• pp.107-113
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• 2009

The energy cost is resulted from the energy use. Its sources are divided into some types and depended on the building use or energy-use type. The energy cost should be affected by the amount of the energy use. The cost could be calculated to consider various factors such as the insulation, heating type, building shape and others. But it can not consider all of the affect factors to the energy cost and need to categorize the factors to the condition for estimating the cost. In this paper, it aimed at providing the estimation model in linear equation and multiple linear regression, utilizing the building exterior condition and management characteristics in apartment housing. Its survey are conducted in two parts of management characteristics and building exterior condition. The correlation analysis is conducted to get rid of the multicolinearity among the inputted factors. The number of linear equation model is 11 and includes the 1st, 2nd and 3rd equation function, power function and others. Among these, it suggested the 2nd and 3rd function and power function in terms of the statistics. In multiple linear regression model, the building volume and management area are inputted to the estimation.

• Korean Journal of Remote Sensing
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• v.6 no.2
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• pp.115-133
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• 1990

Study was made on the spectral characteristics of rock samples including bentonites collected from the northern Ulsan area. The geology of the area consists mainly of sediments of the Kyongsang Series and Bulguksa granite, the Tertiary volcanics, andesites and tuffs. Relative reflectances of meshed samples(2.5~10mm) to BaSO$_4$ are measured at 6 Landsat TM spectral windows (excluding the thermal band) with HHRR, and their reflection charactristics were analysed. In addition, three different data selection schemes including the Eulidean distance, multiple regression, and PCA weight methods were applied to the 30 TM ratio channels, derived from the above 6 bands. The selected data sets were subject to two unsupervised classification techniques(FA and ISODATA) in order to compare the effectiveness for classification of particularly bentonite from others. As a result, in ISODATA analysis the multiple regression model shows the best, followed by the Euliean distances one. The PCA weight model seems to show some confusion. In FA, though difficult for quantitative analysis, the best still seems to be the regression model. Among ratio bands, rations of band 7 or 5 against other bands represent the best contribution in classification of bentonites from others.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.229-243
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• 2022

The effects of monthly meteorological data measured at 11 stations in South Korea on pan coefficient were analyzed to develop the four types of multiple linear regression models for estimating pan coefficients. To evaluate the applicability of developed models, the models were compared with six previous models. Pan coefficients were most affected by air temperature for January, February, March, July, November and December, and by solar radiation for other months. On the whole, for 12 months of the year, the effects of wind speed and relative humidity on pan coefficient were less significant, compared with those of air temperature and solar radiation. For all meteorological stations and months, the model developed by applying 5 independent variables (wind speed, relative humidity, air temperature, ratio of sunshine duration and daylight duration, and solar radiation) for each station was the most effective for evaporation estimation. The model validation results indicate that the multiple linear regression models can be applied to some particular stations and months.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.31
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• pp.1-10
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• 1994

The purpose of this study is to search for the most effective forecasting model for condenser with independent demand among the quantitative methods such as Brown's exponential smoothing method, Box-Jenkins method, and multiple regression analysis method. The criterion for the comparison of the above models is mean squared error(MSE). The fitting results of these three methods are as follows. 1) Brown's exponential smoothing method is the simplest one, which means the method is easy to understand compared to others. But the precision is inferior to other ones. 2) Box-Jenkins method requires much historic data and takes time to get to the final model, although the precision is superior to that of Brown's exponential smoothing method. 3) Regression method explains the correlation between parts with similiar demand pattern, and the precision is the best out of three methods. Therefore, it is suggested that the multiple regression method is fairly good in precision for forecasting our item and that the method is easily applicable to practice.