• Journal of the Korean Society of Clothing and Textiles
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• v.28 no.5
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• pp.658-667
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• 2004

The purpose of this study was to examine factors affecting compulsive buying behavior purchasing behavior. Four hundred female college students who have purchased fashion products via Internet shopping or TV home shopping were surveyed. For data analysis, descriptive statistics, factor analysis, t-test, and multiple regression analysis were used. As the results, 16.3% of respondents were revealed as compulsive buyers. For materialism instrument, four factors of materialism were found and labeled as 'material'. 'consumption', 'happiness', and 'economic value' factors. Also, two factors of entertainment pursuit were also identified as 'change' and 'adventure' pursuit. There were statistically significant differences between compulsive buyers and non-compulsive buyers in terms of materialism, success, and change pursuit, self-esteem, compensation, impulsive purchasing, and binge eating. Compulsive consumption scores were correlated to higher materialism, compensatory, impulsive purchasing, binge eating, shifting, entertainment pursuit, and lower self-esteem. Also, results of multiple regression revealed that compensatory, binge eating, and happiness pursuit were significantly to related to compulsive buying.

• Tuberculosis and Respiratory Diseases
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• v.67 no.2
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• pp.105-112
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• 2009

Background: Solitary pulmonary nodules (SPN) are encountered incidentally in 0.2% of patients who undergo chest X-ray or chest CT. Although SPN has malignant potential, it cannot be treated surgically by biopsy in all patients. The first stage is to determine if patients with SPN require periodic observation and biopsy or resection. An important early step in the management of patients with SPN is to estimate the clinical pretest probability of a malignancy. In every patient with SPN, it is recommended that clinicians estimate the pretest probability of a malignancy either qualitatively using clinical judgment or quantitatively using a validated model. This study examined whether Bayesian analysis or multiple logistic regression analysis is more predictive of the probability of a malignancy in SPN. Methods: From January 2005 to December 2008, this study enrolled 63 participants with SPN at the Kangnam Sacred Hospital. The accuracy of Bayesian analysis and Bayesian analysis with a FDG-PET scan, and Multiple logistic regression analysis was compared retrospectively. The accurate probability of a malignancy in a patient was compared by taking the chest CT and pathology of SPN patients with <30 mm at CXR incidentally. Results: From those participated in study, 27 people (42.9%) were classified as having a malignancy, and 36 people were benign. The result of the malignant estimation by Bayesian analysis was 0.779 (95% confidence interval [CI], 0.657 to 0.874). Using Multiple logistic regression analysis, the result was 0.684 (95% CI, 0.555 to 0.796). This suggests that Bayesian analysis provides a more accurate examination than multiple logistic regression analysis. Conclusion: Bayesian analysis is better than multiple logistic regression analysis in predicting the probability of a malignancy in solitary pulmonary nodules but the difference was not statistically significant.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.10
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• pp.1633-1641
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• 2020

Objective: The objective of this study was to develop a model for estimating the carcass weight of Hanwoo cattle as a function of body measurements using three different modeling approaches: i) multiple regression analysis, ii) partial least square regression analysis, and iii) a neural network. Methods: Data from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal Science in South Korea. Among the 372 variables in the raw data, 20 variables related to carcass weight and body measurements were extracted to use in multiple regression, partial least square regression, and an artificial neural network to estimate the cold carcass weight of Hanwoo cattle by any of seven body measurements significantly related to carcass weight or by all 19 body measurement variables. For developing and training the model, 100 data points were used, whereas the 34 remaining data points were used to test the model estimation. Results: The R² values from testing the developed models by multiple regression, partial least square regression, and an artificial neural network with seven significant variables were 0.91, 0.91, and 0.92, respectively, whereas all the methods exhibited similar R² values of approximately 0.93 with all 19 body measurement variables. In addition, relative errors were within 4%, suggesting that the developed model was reliable in estimating Hanwoo cattle carcass weight. The neural network exhibited the highest accuracy. Conclusion: The developed model was applicable for estimating Hanwoo cattle carcass weight using body measurements. Because the procedure and required variables could differ according to the type of model, it was necessary to select the best model suitable for the system with which to calculate the model.

• The Journal of the Korea Contents Association
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• v.19 no.9
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• pp.181-194
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• 2019

The purpose of this study is to investigate the effect of physical environment of Japanese restaurant on customer satisfaction, revisit intention and word of mouth intention. For this purpose, data were collected from 341 adult males and females who had used Japanese restaurants in Daegu. SPSS program frequency analysis, factor analysis, regression analysis and multiple regression analysis were used for data analysis. Through the factor analysis, the physical environment of the Japanese specialty restaurant was identified as a component of comfort, aesthetics, and convenience. As a result of the multiple regression analysis, the three components of the physical environment have positive effects on customer satisfaction, return visit intention, and word of mouth intention. The results of regression analysis showed that customer satisfaction had a positive effect on revisit and word of mouth intention. Through the results of this study, we confirmed the physical and environmental impacts of the specialty restaurants and suggested the practical and theoretical implications.

• The Journal of the Korea Contents Association
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• v.19 no.10
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• pp.698-711
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• 2019

The purpose of this study was to investigate the effect of social servicescape on customer satisfaction, customer trust, and customer loyalty. Data were collected from 311 adults who lived in Daegu where they had used Japanese restaurants. For data analysis, frequency analysis, factor analysis, regression analysis and multiple regression analysis were used. Through the factor analysis, the social servicescape of Japanese restaurant was identified as two components of human service and customer similarity. As a result of the multiple regression analysis, two components of social servicescape have positive effects on customer satisfaction and customer trust, and have a partial positive effect on customer loyalty. The results of regression analysis showed that customer satisfaction had a positive effect on customer trust and customer loyalty. In addition, customer trust has a positive effect on customer loyalty. The results of this study confirmed the influence of social servicescape on Japanese specialty restaurants and suggested practical and theoretical implications.

• The Korean Journal of Health Service Management
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• v.8 no.2
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• pp.11-22
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• 2014

This study purposed to analyze the regional variation of the local-out rates of inpatient services. Multiple data sources collected from National Health Insurance Corporation and statistics Korea were merged to produce the analysis data set. The unit of analysis in this study was city, Gun, Gu, and all of them were included in analysis. The dependent variable measured the local-out rate of inpatient cost in study regions. Local environments were measured by variables in three dimensions: provider factors, socio-demographic factors, and health status. Along with the traditional ordinary least square (OLS) based regression model, geographically weighted regression (GWR) model were applied to test their effects. SPSS v21 and ArcMap v10.2 were applied for the statistical analysis. Results from OLS regression showed that most variables had significant relationships with the local-out rate of inpatient services. However, some variables had shown diverse directions in regression coefficients depending on regions in GWR. This implied that the study variables might not have consistent effects and they may varied depending the locations.

• Journal of the Korea Society for Simulation
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• v.21 no.3
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• pp.79-88
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• 2012

Recently, the study in efficient operation, maintenance, and equipment-design have been growing rapidly in military industry to meet the required missions. Through out these studies, the importance of Concurrent Spare Parts(CSP) are emphasized. The CSP, which is critical to the operation and maintenance to enhance the availability, is offered together when a equipment is delivered. Despite its significance, th responsibility for determining the range and depth of CSP are done from administrative decision rather than engineering analysis. The purpose of the paper is to optimize the number of CSP per item using simulation and multiple regression. First, the result, as the change of operational availability, was gained from changing the number of change in simulation model. Second, mathematical regression was computed from the input and output data, and the number of CSP was optimized by multiple regression and linear programming; the constraint condition is the cost for optimization. The advantage of this study is to respond with the transition of constraint condition quickly. The cost per item is consistently altered in the development state of equipment. The speed of analysis, that simulation method is continuously performed whenever constraint condition is repeatedly altered, would be down. Therefore, this study is suitable for real development environment. In the future, the study based on the above concept improves the accuracy of optimization by the technical progress of multiple regression.

• Journal of the Korean Data and Information Science Society
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• v.20 no.2
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• pp.321-328
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• 2009

In regression analysis, the ordinary least squares estimates of regression coefficients become poor, when the correlations among predictor variables are high. This phenomenon, which is called multicollinearity, causes serious problems in actual data analysis. To overcome this multicollinearity, many methods have been proposed. Ridge regression, shrinkage estimators and methods based on principal component analysis (PCA) such as principal component regression (PCR) and latent root regression (LRR). In the last decade, many statisticians discussed sensitivity analysis (SA) in ordinary multiple regression and same topic in PCR, LRR and logistic principal component regression (LPCR). In those methods PCA plays important role. Many statisticians discussed SA in PCA and related multivariate methods. We introduce the method of PCR and LRR. We also introduce the methods of SA in PCR and LRR, and discuss the properties of SA in PCR and LRR.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.1
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• pp.135-144
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• 2009

The objective of this study is to examine the effect of hydrological factors on critical durations, and to analyze the relationship between the watershed characteristics and the critical duration of design rainfall in the medium sized catchments. Hydrological factors are used to return period, probable intensity formula, hydrograph method, effective rainfall and temporal pattern of design rainfall. Hydrologic analysis has done over the 44 medium sized catchments with $50{\sim}5,000{\beta}{\yen}$. Watershed characteristics such as catchment area, channel length, channel slope, catchment slope, time to peak, concentration of time and curve number were used to simulate correlation analysis. All of hydrological factors except return period influence to the critical duration of design rainfall. Also, it is revealed that critical duration is influenced by the watershed characteristics such as area, channel length, channel slope and catchment slope. Multiple regression analysis using watershed characteristics is carried out for the estimation of relationship among these. And the 7 type equations are proposed by the multiple regression using watershed characteristics and critical duration of design rainfall. The determination coefficient of multiple regression equations shows $0.96{\sim}0.97$.

• Korean Security Journal
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• no.43
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• pp.99-120
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• 2015

This research aimed at analyzing health behavior of private security guards applying planned behavioral theory. In order to achieve the above purpose, this research conducted purposive sampling on the security guards who live in Seoul Gyeonggi region. Excluding unfaithful response and abnormal outlier, material of 187 persons was used for analysis. As the concrete analysis method, multiple regression analysis and logistic regression analysis to presume exploratory factory analysis(EFA), Polyserial Exploratory Factor Analysis(EFA), Polyserial correlation analysis, and causal relationship between each variable. The result can be summarized as follows. First, attachment, attitude subjective standard on behavior, perceived behavioral control appeared to positively influence affirmative(+) effect on health behavior continuance will. Second, attachment had no meaningful influence attitude toward behavior. Third, attachment had affirmative(+) influence on health behavior continuance will. Fourth, perceived behavioral control had affirmative(+) influence on realization of health behavior, possibility of practising health behavior increased by about 62.9% when perceived behavioral control increased by 1 unit.