• Proceedings of the Korean Society for Quality Management Conference
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• 2006.04a
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• pp.473-478
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• 2006

Empirical analysis was based on the data of 692 hotel employees from Seoul and Gyung-gi province. To test the relationship between job characteristics and service quality, multiple regression analysis was applied, and hierarchial regression analysis(Cohen & Cohen, 1983) applied to test the moderating effects of gender and tenure. Multiple regressional analysis showed that job importance, job variety, and feedback had an positive impact on tangibility, reliability, responsiveness, assurance, empathy, but autonomy had a negative impact on tangibility, reliability, responsiveness, assurance. Hierarchical regressional analysis showed that gender had moderating effects on 4 relationships between job characteristics and service quality, and tenure had moderating effects on 3 relationships.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.2
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• pp.358-365
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• 2001

The purpose of this study was to investigate the relation between various aspects of multiple body image and clothing behavior i.e. individuality/self expression, body improvement, social approval, sex appeal. The data were collected from 498 female university students in Seoul and Kyong Ki Province and analyzed using factor analysis, Pearsons correlations, reliability test, analysis of variance, and multiple regression analysis. The results of this study were as follows: 1) Four dimensions of multiple body image were identified : appearance, body attractiveness, degree of fitness and atheletic skill. 2) Perception on appearance and fitness aspect of multiple body image has a positive correlation with all aspects of clothing behavior i.e. individuality/self expression, body improvement, social approval and sex appeal of clothing behavior. Body attractiveness and atheletic skill of multiple body image also had a positive correlation with individuality/self expression, and sex appeal.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.755-768
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• 2017

As Big Data becomes the core of the fourth industrial revolution, big data-based processing and analysis capabilities are expected to influence the company's future competitiveness. Comparative studies of RHadoop and RHIPE that integrate R and Hadoop environment, have not been discussed by many researchers although RHadoop and RHIPE have been discussed separately. In this paper, we constructed big data platforms such as RHadoop and RHIPE applicable to large scale data and implemented the machine learning algorithms such as multiple regression and logistic regression based on MapReduce framework. We conducted a study on performance and scalability with those implementations for various sample sizes of actual data and simulated data. The experiments demonstrated that our RHadoop and RHIPE can scale well and efficiently process large data sets on commodity hardware. We showed RHIPE is faster than RHadoop in almost all the data generally.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.1-6
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• 2019

In this study, the development of a weight estimation model of electronic scale with nonlinear characteristics is presented using polynomial regression analysis. The output voltage of the load cell was measured directly using the reference mass. And a polynomial regression model was obtained using the matrix and curve fitting function of MS Office Excel. The weight was measured in 100g units using a load cell electronic scale measuring up to 5kg and the polynomial regression model was obtained. The error was calculated for simple($1^{st}$), $2^{nd}$ and $3^{rd}$ order polynomial regression. To analyze the suitability of the regression function for each model, the coefficient of determination was presented to indicate the correlation between the estimated mass and the measured data. Using the third order polynomial model proposed here, a very accurate model was obtained with a standard deviation of 10g and the determinant coefficient of 1.0. Based on the theory of multi regression model presented here, it can be used in various statistical researches such as weather forecast, new drug development and economic indicators analysis using logistic regression analysis, which has been widely used in artificial intelligence fields.

• Journal of Korean Society for Atmospheric Environment
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• v.14 no.6
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• pp.631-642
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• 1998

Fall-out particles were collected by the modified British deposit gauges at 35 sampling sites in Suwon area from January to November, 1996. Twenty chemical species (Al. Ba, Cd, Cr, K, Pb, Sb, Zn, Cu, Fe, Ni, V, F-, Cl-, NO3-, 5042-, Na+, NH4+, Mg2+, and Ca2+) were analyzed by AAS and If. The purposes of this study were to estimate qualitatively various emission sources of the fell-out particle by applying multivariate statistical techniques such as factor analysis, multiple regression analysis, and discriminant analysis. During the study, outlier sites were determined by a z-score method. Cl-, Na+, Mg2+, and SO42- were highly correlated due to their common marine related source. Wind speed was the most influential factor for the deposition fluxes of the particle itself and all the chemical species as well. When applying the factor analysis, 8 source patterns were qualitatively obtained, such as marine source, soil source, oil burning source, Cr related source, tire source, Cd related source, agriculture source, and F- related source. As a result of the multiple regression analysis, we could suggest that some chemical compounds may possibly exist in the form of CaSO4, NaN03, NaCl, MgC12, (NH4)2SO4, NaF, and CaCl2 in the fall-out particles. Finally, spatial and seasonal classification study performed by a discriminant analysis showed th.at SO42-, Ca2+, Cl-, and Fe were dominant in the group of spatial pattern; however, SO42-, Cl-, Al, and V were in the group of seasonal pattern.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.3
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• pp.395-406
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• 2015

This research carried out an analysis on input cost and leakage reduction effect by leakage reduction method, focusing on the project for establishing an optimal water pipe network management system in the Taebaek region, which has been executed annually since 2009. Based on the result, optimal cost-benefit analysis models for water distribution network rehabilitation project were developed using DEA(data envelopment analysis) and multiple regression analysis, which have been widely utilized for efficiency analysis in public and other projects. DEA and multiple regression analysis were carried out by applying 4 analytical methods involving different ratios and costs. The result showed that the models involving the analytical methods 2 and 4 were of low significance (which therefore were excluded), and only the models involving the analytical methods 1 and 3 were suitable. From the result it was judged that the leakage management method to be executed with the highest priority for the improvement of revenue water ratio was installation of pressure reduction valve, followed by replacement of water distribution pipe, replacement of water supply pipe, and then leakage detection and repair; and that the execution of leakage management methods in this order would be most economical. In addition, replacement of water meter was also shown to be necessary in case there were a large number of defective water meters.

• Korean Journal of Human Ecology
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• v.20 no.6
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• pp.1135-1151
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• 2011

This study examined green consumption behavior according to the lifestyles of college students. The data were collected from 314 college students in Yeungnam region by a self-administered questionnaire. Frequencies, Cronbach's alpha, factor analysis, cluster analysis, chi-square tests, one-way analysis of variance, Duncan's multiple range tests, Pearson's correlation analysis, and multiple regression analyses were conducted by SPSS Windows V.18.0. According to the result of factor analysis, lifestyles were categorized into six factors: thrift-saving type, enthusiastic activity type, brand ostentation type, freedom-seeking type, material oriented type, and practice-seeking type. Cluster analysis showed respondents belonged to one of four groups: thrift practice group, indifference group, freedom-seeking group, and material ostentation group. The levels of green purchase behavior, green usage behavior and green disposal behavior of the respondents was not high. The thrift practice group showed higher levels of green purchase behavior, green usage behavior, and green disposal behavior. Finally, according to multiple regression analyses, environmental consciousness, knowledge about green consumption, lifestyle groups were the significant factors affecting green consumption behaviors. These results imply that green consumption education for college students should be activated to induce green life.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.665-676
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• 2013

The present study analyzed the KOCCA's Game Immersion data to explore the relationship between psychological traits and game immersion. Psychological traits were classified into four categories; emotional stability, self-esteem, willpower, and happiness. These were put into the multiple regression analysis as the dependent variables with age, sex (dummy), economic status of the homes, variety of leisure activities, friend and family relationship, and time to play games. Four types of immersion (psychological unstability, bad relationship, interrupted daily lives, and failure to control game time and desire) were put into the multiple regression analysis as the independent variables. The multiple regression analysis indicated that the dependent variables predicted the game immersion. Among the psychological traits emotional stability was the strongest factor (negative) that influences the game immersion. Next powerful indicator (negative) among the psychological traits was self-esteem. Based on the findings, measures to alleviate the problems of game immersion and ideas for further research were suggested.

• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.891-897
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• 2021

There is a need for a technology that can quickly and accurately analyze soil carbon contents. Existing soil carbon analysis methods are cumbersome in terms of professional manpower requirements, time, and cost. It is against this background that the present study leverages the soil physical properties of color and water content levels to develop a model capable of predicting the carbon content of soil sample. To predict the total carbon content of soil, the RGB values, water content of the soil, and lux levels were analyzed and used as statistical data. However, when R, G, and B with high correlations were all included in a multiple regression analysis as independent variables, a high level of multicollinearity was noted and G was thus excluded from the model. The estimates showed that the estimation coefficients for all independent variables were statistically significant at a significance level of 1%. The elastic values of R and B for the soil carbon content, which are of major interest in this study, were -2.90 and 1.47, respectively, showing that a 1% increase in the R value was correlated with a 2.90% decrease in the carbon content, whereas a 1% increase in the B value tallied with a 1.47% increase in the carbon content. Coefficient of determination (R²), root mean square error (RMSE), and mean absolute percentage error (MAPE) methods were used for regression verification, and calibration samples showed higher accuracy than the validation samples in terms of R² and MAPE.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.313-320
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• 2013

In this study of tourism destination brand equity and self-consistent interstellar significant attachment relationships and brand equity and behavioral intention was to determine the effect on the relationship. First, the tourism destination brand equity of the self-consistent sex tourists will have a significant impact is to test the hypothesis of a multiple regression analysis was conducted brand equity, loyalty, self image and gender matched only indicates the relationship was significant. Second, self Correspondence tourist destination tourists will have a significant effect on attachment. In order to verify the hypothesis that the multiple regression analysis was conducted for self-Correspondence attachment was significantly related shows. Third, the attachment of tourist destinations for travelers of action also will have a significant impact on. Hypotheses multiple regression analysis was conducted to attachment behavior also shows significant relationship was about.