• Journal of the Korean Data and Information Science Society
• /
• v.27 no.5
• /
• pp.1337-1347
• /
• 2016

This study investigates factors affecting the number of insurance solicitor's turnovers of life insurance companies based on questionnaire about them. Since the response variable which is the number of insurance solicitor's turnovers is count data, it is analyzed by Poisson regression which is one of generalized regression. When work year in current company, which is direct influential factor on the number of insurance solicitor's turnovers, is controlled, affiliated corporation has been found to be the most influential factor. In addition, age, motivation to work as financial planner, monthly income, a number of average new contract per month, and final education have been identified to be important factors. If insurance solicitor's occupant organization is large company, the number of turnovers becomes small, but if the organization is general agent(GA), it becomes larger. When insurance solicitor's age is high, the number of insurance solicitor's turnovers are reduced. If the motivation to become a financial planner is due to acquaintance such as family and relatives, the number of turnovers becomes small.

• Fashion & Textile Research Journal
• /
• v.17 no.2
• /
• pp.265-277
• /
• 2015

The aim of this study was to successfully reflect human body changes on the transformation of the virtual body within 3D virtual fitting spaces. For this purpose, existing problems of shape transformation of the virtual body were analyzed and regression equations which provides useful basic data for transformation of the virtual body that can be applied usefully to the 3D virtual fitting system was developed. Necessary data for the analyses were body measurement and 3D scan data of women with average physical form between the ages of 40 through 49. The reason that we used human body changes of the female subjects in their forties was based on the recognition that fundamental female body changes start to occur from age of forty. Body shapes were largely divided into 3 groups according to obesity which was found to be the biggest factor of shape change. Seven factors were extracted based on factor analysis of 47 body measurement categories and regression equations were created to extract specific measurements for each BMI group based on these seven factors. The major contribution of this paper can be summarized as follows. First, the regression equations to extract specific measurements based on the 7 representative variables remediated existing problem of virtual bodies as it increased the number of body shape transformation areas. Second, the regression equations helped to overcome the problem of current failing to reflecting changes in body cross-section shape based on simple girth measurements based on analysis of cross-section distances.

• Journal of the Korean Society of Propulsion Engineers
• /
• v.14 no.2
• /
• pp.29-38
• /
• 2010

The experimental study on paraffin wax/LDPE blended fuel for hybrid rocket was performed. Various combustion characteristics of blended fuel were compared with pure paraffin, HTPB, HDPE and SP-1a fuel in order to evaluate the performance of blended fuel. The regression rate of lab-scale and large-scale motor using pure paraffin fuel was increased by 10.2 and 9.8 factor when respectively compared to that of HDPE. The regression rate factor of blended fuel was 3.4 in which the regression rate of blended fuel was higher than that of HTPB and HDPE, but lower than that of pure paraffin, SP-1a fuel. The values of characteristic velocity and specific impulse of blended fuel was higher than those of pure paraffin, HTPB and HDPE, and almost the same as SP-1a fuel. As these results, it was confirmed that blended fuel can be an effective solid fuel for hybrid rocket.

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

• The Journal of the Korea Contents Association
• /
• v.19 no.10
• /
• pp.698-711
• /
• 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 Journal of the Korea Contents Association
• /
• v.19 no.9
• /
• pp.181-194
• /
• 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.

• Atmosphere
• /
• v.24 no.4
• /
• pp.491-502
• /
• 2014

As a part of the KIAPS (Korea Institute of Atmospheric Prediction Systems) Package for Observation Processing (KPOP), we have developed the modules for Advanced Microwave Sounding Unit-A (AMSU-A) pre-processing and its bias correction. The KPOP system calculates the airmass bias correction coefficients via the method of multiple linear regression in which the scan-corrected innovation and the thicknesses of 850~300, 200~50, 50~5, and 10~1 hPa are respectively used for dependent and independent variables. Among the four airmass predictors, the multicollinearity has been shown by the Variance Inflation Factor (VIF) that quantifies the severity of multicollinearity in a least square regression. To resolve the multicollinearity, we adopted simple linear regression and Principal Component Regression (PCR) to calculate the airmass bias correction coefficients and compared the results with those from the multiple linear regression. The analysis shows that the order of performances is multiple linear, principal component, and simple linear regressions. For bias correction for the AMSU-A channel 4 which is the most sensitive to the lower troposphere, the multiple linear regression with all four airmass predictors is superior to the simple linear regression with one airmass predictor of 850~300 hPa. The results of PCR with 95% accumulated variances accounted for eigenvalues showed the similar results of the multiple linear regression.

• Geomechanics and Engineering
• /
• v.13 no.2
• /
• pp.237-254
• /
• 2017

Estimation of slope stability is a very important task in geotechnical engineering. However, its estimation using conventional and soft computing methods has several drawbacks. Use of conventional limit equilibrium methods for the evaluation of slope stability is very tedious and time consuming, while the use of soft computing approaches like Artificial Neural Networks and Fuzzy Logic are black box approaches. Multiple Regression (MR) analysis provides an alternative to conventional and soft computing methods, for the evaluation of slope stability. MR models provide a simplified equation, which can be used to calculate critical factor of safety of slopes without adopting any iterative procedure, thereby reducing the time and complexity involved in the evaluation of slope stability. In the present study, a multiple regression model has been developed and tested its accuracy in the estimation of slope stability using real field data. Here, two separate multiple regression models have been developed for dry and wet slopes. Further, the accuracy of these developed models have been compared and validated with respect to conventional limit equilibrium methods in terms of Mean Square Error (MSE) & Coefficient of determination ($R^2$). As the developed MR models here are not based on any region specific data and covers wide range of parametric variations, they can be directly applied to any real slopes.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.13 no.3
• /
• pp.77-82
• /
• 2014

In this study, we carried out the interrupted cutting of carbon steel for a machine structure (SM45C) with a CVD-coated tool and conducted an ANOVA test and a confidence interval analysis to find factors influence the surface roughness and to obtain a regression equation. We found that factor which mostly affects the surface roughness during interrupted cutting was the feed rate. The cutting speed and depth of the cut only had small effect on the surface roughness. From the result of a multi-regression analysis during an interrupted cutting experiment, we obtained regression equation. Its coefficient of determination was 0.918, indicating that the regression equation was predictable. Compared to continuous cutting, if the feed rate increases, the surface roughness will also increase during interrupted cutting.

• Structural Engineering and Mechanics
• /
• v.45 no.6
• /
• pp.837-851
• /
• 2013

In construction industry, strength is a primary criterion in selecting a concrete for a particular application. The concrete used for construction gains strength over a long period of time after pouring the concrete. The characteristic strength of concrete is defined as the compressive strength of a sample that has been aged for 28 days. Neither waiting for 28 days for such a test would serve the rapidity of construction, nor would neglecting it serve the quality control process on concrete in large construction sites. Therefore, rapid and reliable prediction of the strength of concrete would be of great significance. On this backdrop, the method is proposed to establish a predictive relationship between properties and proportions of ingredients of concrete, compaction factor, weight of concrete cubes and strength of concrete whereby the strength of concrete can be predicted at early age. Multiple regression analysis was carried out for predicting the compressive strength of concrete containing Portland Pozolana cement using statistical analysis for the concrete data obtained from the experimental work done in this study. The multiple linear regression models yielded fairly good correlation coefficient for the prediction of compressive strength for 7, 28 and 40 days curing. The results indicate that the proposed regression models are effectively capable of evaluating the compressive strength of the concrete containing Portaland Pozolana Cement. The derived formulas are very simple, straightforward and provide an effective analysis tool accessible to practicing engineers.