• Title/Summary/Keyword: the multiple regression analysis

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Moderating effect Switching Barrier on Coffee-shop customer Satisfaction and Loyalty (커피전문점에서 전환장벽을 고려한 고객만족과 충성의 관계)

  • Kim, Pan-Su;Han, Jang-Hyeop
    • Proceedings of the Safety Management and Science Conference
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    • 2011.11a
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    • pp.683-694
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    • 2011
  • This study analysed impact of service quality on customer satisfaction and loyalty in the take-out coffee shop. The switching barrier was also studied as a moderating effect. Particularly, this study focused on relationships between customer loyalty and switching barriers. A lot of previous studies interest only in customers satisfaction. This study also analysed relationships among service quality, customer satisfaction, switching barriers and brand loyalty. Eventually, service quality significantly affects customer satisfaction, moderating effects, brand loyalty and marketing performance. SERVQUAL model which was established by PZB (1988) was used as a service quality factors. The impact on customer satisfaction was analysed using multiple regression analysis. Simple regression analysis was used to find effects of customer satisfaction and customer loyalty. Additional factors of switching barriers was classified based on previous studies. Hierarchical multiple regression analysis was used to find factors of customer loyalty among switching barriers. In the result, we can find that the importance of tangibles, responsiveness in service quality factors and contract cost, search cost and continuous cost in moderating effects.

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Prediction of Surface Roughness of Al7075 on End-Milling Working Conditions by Non-linear Regression Analysis (비선형 회귀분석에 의한 엔드밀 가공조건에 따른 Al7075의 표면정도 예측)

  • Cho, Yon-Sang;Park, Heung-Sik
    • Tribology and Lubricants
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    • v.26 no.6
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    • pp.329-335
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    • 2010
  • Recently, the End-milling processing is needed the high-precise technique to get a good surface roughness and rapid time in manufacturing of precision machine parts and electronic parts. The optimum surface roughness has an effect on end-milling working condition such as, cutting direction, spindle speed, feed rate and depth of cut, and so on. It needs to form the correlation of working conditions and surface roughness. Therefore this study was carried out to presume of surface roughness on end-milling working condition of Al7075 by regression analysis. The results was shown that the coefficient of determination($R^2$) of regression equation had a fine reliability of 87.5% and nonlinear regression equation of surface rough was made by multiple regression analysis.

Analysis of Success Factors for Mobile Commerce using Text Mining and PLS Regression

  • Kim, Yong-Hwan;Kim, Ja-Hee;Park, Ji hoon;Lee, Seung-Jun
    • Journal of the Korea Society of Computer and Information
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    • v.21 no.11
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    • pp.127-134
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    • 2016
  • In this paper, we propose factors that influence on the mobile commerce satisfaction conducted by data mining and a PLS regression analysis. We extracted the most frequent words from mobile application reviews in which there are a large number of user's requests. We employed the content analysis to condense the large number of texts. We took a survey with the categories by which data are condensed and specified as factors that influence on the mobile commerce satisfaction. To avoid multicollinearity, we employed a PLS regression analysis instead of using a multiple regression analysis. Discovered factors that are potential consequences of customer satisfaction from direct requests by customers, the result may be an appropriate indicator for the mobile commerce market to improve its services.

FACTORS AFFECTING PATIENTS' DECISION-MAKING FOR DENTAL PROSTHETIC TREATMENT

  • Jung, Hyo-Kyung;Kim, Han-Gon
    • The Journal of Korean Academy of Prosthodontics
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    • v.46 no.6
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    • pp.610-619
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    • 2008
  • STATEMENT OF PROBLEM: Factors affecting patients' decision-making for dental prosthetic treatment should be examined in terms of understanding improving patients' oral health. PURPOSE: The main purpose of this dissertation was to investigate patients' dental prosthetic treatment and factors affecting patients' decision-making for dental prosthesis treatment in Deagu and Gyungbook areas. MATERIAL AND METHODS: This study was based on the preliminary survey of dental patients conducted from July 1 to August 31 in 2006. A total of 700 questionnaires had been distributed and 640 were collected. 629 questionnaires were used for the statistical analysis. Descriptive and inferential statistics, such as frequencies, cross tabulation analysis, correlation analysis, logistic regression analysis, and multiple regression analysis were introduced. In the multiple regression analysis and logistic regression analysis, twenty-two independent variables were employed to explore the factors which have impacts on decision-making and satisfaction. RESULTS: The results of this dissertation are as follows: Logistic regression analysis turned out that monthly income, age, degree of expectation, marital status, and employer-insured policy of national insurance statistically increased the odds of decision-making of dental prosthesis treatment. But educational attainment decreased the odds ratio of the decision-making of dental prosthesis treatment. However, the rest independent variables do not have statistically significant impacts on the decision-making of dental prosthesis treatment CONCLUSION: Among independent variables, marital status had the most significant influence on the decision making of dental prosthesis treatment. Finally, suggestions for the future study and policy implications to improve satisfaction of the patients' dental prosthetic treatment were discussed.

Estimation of Water Quality of Fish Farms using Multivariate Statistical Analysis

  • Ceong, Hee-Taek;Kim, Hae-Ran
    • Journal of information and communication convergence engineering
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    • v.9 no.4
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    • pp.475-482
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    • 2011
  • In this research, we have attempted to estimate the water quality of fish farms in terms of parameters such as water temperature, dissolved oxygen, pH, and salinity by employing observational data obtained from a coastal ocean observatory of a national institution located close to the fish farm. We requested and received marine data comprising nine factors including water temperature from Korea Hydrographic and Oceanographic Administration. For verifying our results, we also established an experimental fish farm in which we directly placed the sensor module of an optical mode, YSI-6920V2, used for self-cleaning inside fish tanks and used the data measured and recorded by a environment monitoring system that was communicating serially with the sensor module. We investigated the differences in water temperature and salinity among three areas - Goheung Balpo, Yeosu Odongdo, and the experimental fish farm, Keumho. Water temperature did not exhibit significant differences but there was a difference in salinity (significance <5%). Further, multiple regression analysis was performed to estimate the water quality of the fish farm at Keumho based on the data of Goheung Balpo. The water temperature and dissolved-oxygen estimations had multiple regression linear relationships with coefficients of determination of 98% and 89%, respectively. However, in the case of the pH and salinity estimated using the oceanic environment with nine factors, the adjusted coefficient of determination was very low at less than 10%, and it was therefore difficult to predict the values. We plotted the predicted and measured values by employing the estimated regression equation and found them to fit very well; the values were close to the regression line. We have demonstrated that if statistical model equations that fit well are used, the expense of fish-farm sensor and system installations, maintenances, and repairs, which is a major issue with existing environmental information monitoring systems of marine farming areas, can be reduced, thereby making it easier for fish farmers to monitor aquaculture and mariculture environments.

Development of a Multiple Linear Regression Model to Analyze Traffic Volume Error Factors in Radar Detectors

  • Kim, Do Hoon;Kim, Eung Cheol
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.39 no.5
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    • pp.253-263
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    • 2021
  • Traffic data collected using advanced equipment are highly valuable for traffic planning and efficient road operation. However, there is a problem regarding the reliability of the analysis results due to equipment defects, errors in the data aggregation process, and missing data. Unlike other detectors installed for each vehicle lane, radar detectors can yield different error types because they detect all traffic volume in multilane two-way roads via a single installation external to the roadway. For the traffic data of a radar detector to be representative of reliable data, the error factors of the radar detector must be analyzed. This study presents a field survey of variables that may cause errors in traffic volume collection by targeting the points where radar detectors are installed. Video traffic data are used to determine the errors in traffic measured by a radar detector. This study establishes three types of radar detector traffic errors, i.e., artificial, mechanical, and complex errors. Among these types, it is difficult to determine the cause of the errors due to several complex factors. To solve this problem, this study developed a radar detector traffic volume error analysis model using a multiple linear regression model. The results indicate that the characteristics of the detector, road facilities, geometry, and other traffic environment factors affect errors in traffic volume detection.

The Influence Factors on the Activation of Environmental Innovations in Manufacturing Firms (제조기업의 환경혁신에 대한 원인요인과 촉진요인)

  • Choe, Jong-min
    • 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.

Usage of Multiple Regression Analysis in Prediction System of Process Parameters for Arc Robot Welding (아크로봇 용접 공정변수 예측시스템에 다중회귀 분석법의 사용)

  • Lee, Jeong-Ick
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.9 no.4
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    • pp.871-877
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    • 2008
  • It is important to investigate the relationship between weld process parameters and weld bead geometry for adaptive arc robot welding. Howeve, it is difficult to predict an exact back-bead owing to gap in process of butt welding. In this paper, the quantitative prediction system to specify the relationship external weld conditions and weld bead geometry was developed to get suitable back-bead in butt welding which is widely applied on industrial field. Multiple regression analysis for the prediction of process parameters was used as the research method. And, the results of the prediction method were compared and analyzed.

A study on improving leadership of reserve officers' training corps (ROTC) (학군장교 (ROTC) 리더십 향상 방안 연구)

  • Kim, Jung-Su
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.6
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    • pp.1525-1536
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    • 2016
  • ROTC is the system that selects the best students in university and implements the military training of two years and uses them as beginning commander of the army by commissioning as officers. ROTC makes up a very large portion of the army and society in Korea in terms of the size and role. Therefore, it is very important to enhance intangible combat power of the Korean army by improving leadership of primary grade officer from ROTC. The purpose of this paper is to investigate whether or not ROTC candidate training program is providing the right training, so as to improve ROTC's leadership and present improvement plan. First, we divide ROTC candidate training program into three areas and take the 1st and 2nd survey. Then we compare and analyze the actual condition and recognition of investigation using statistical analysis methods such as chi-square test, ANOVA and Duncan's multiple range test. Also, we analyze the element affecting the army leader's three qualities formation which are basic elements of ROTC cadet's leadership using multiple regression analysis.

Analysis of the Effects of Population, Household, and Housing Characteristics on the Status of Empty Houses Using Population Housing Census Data (인구주택 총조사 자료를 이용한 인구, 가구, 주택 특성과 빈집 현황 분석)

  • Lee, Jimin;Choi, Won
    • Journal of The Korean Society of Agricultural Engineers
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    • v.62 no.5
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    • pp.1-13
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    • 2020
  • The empty houses' problem is important in the local revitalization and local sustainability, and these phenomenon caused by various factors of the region. The population and housing census data are the most effective data available to study this phenomenon by small regions. In this study, logistic regression and multiple regression analysis were performed to understand the effects of population, household, and housing characteristics on empty houses using population and housing census data. Also, the scale and direction of the effect of each characteristic in large cities, small cities, and rural areas were compared. As results, there was a slight difference between cities and province regions in the district and housing characteristic variables. In the comparison of Eup-Myeon-Dong, the affected variables were different in the Dong and Myeon areas. The significance of this study is to examine the effect of the characteristics of population and housing on the vacant houses and to confirm that the factors affecting different regions.