• Educational Technology International
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• v.15 no.2
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• pp.71-88
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• 2014

A variety of studies to predict students' performance have been conducted since educational data such as web-log files traced from Learning Management System (LMS) are increasingly used to analyze students' learning behaviors. However, it is still challenging to predict students' learning achievement in blended learning environment where online and offline learning are combined. In higher education, diverse cases of blended learning can be formed from simple use of LMS for administrative purposes to full usages of functions in LMS for online distance learning class. As a result, a generalized model to predict students' academic success does not fulfill diverse cases of blended learning. This study compares two blended learning classes with each prediction model. The first blended class which involves online discussion-based learning revealed a linear regression model, which explained 70% of the variance in total score through six variables including total log-in time, log-in frequencies, log-in regularities, visits on boards, visits on repositories, and the number of postings. However, the second case, a lecture-based class providing regular basis online lecture notes in Moodle show weaker results from the same linear regression model mainly due to non-linearity of variables. To investigate the non-linear relations between online activities and total score, RF (Random Forest) was utilized. The results indicate that there are different set of important variables for the two distinctive types of blended learning cases. Results suggest that the prediction models and data-mining technique should be based on the considerations of diverse pedagogical characteristics of blended learning classes.

• Journal of Distribution Science
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• v.17 no.10
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• pp.27-39
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• 2019

Purpose - This study explores the relationships between e-Marketing and perceived service quality with brand trust and loyalty towards the brand of mobile telephone operators in Kosovo. Research design, data, and methodology - The conceptual model of four constructs was developed, while each construct consisted of the set of variables measured by using five-point Likert scale. The primary data was collected via an online survey through SurveyMonkey, distributed via Facebook and email. The collected data were cleaned, validated and tested for its consistency through Cronbach's Alpha; ensured that five assumptions of multiple linear regression are met. To assess the relationships between outcome variable and dependent variables of the model, performed the bivariate correlation, simple, multiple linear and hierarchical regression. Results - Perceived service quality has a positive significant effect on brand trust and loyalty. e-Marketing presents moderating direct effect on brand loyalty, and slightly higher effect through brand trust of the mobile telephone operators in Kosovo. Conclusions - The perceived service quality and brand trust are key determinants in achieving brand loyalty in telecommunication industry, followed by e-Marketing as means to creating expectations, delivering on promise to meet customers' perceptions for service quality with a purpose of building trust, that leads to loyalty towards the brands of mobile operators in Kosovo.

• Journal of the Korean earth science society
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• v.28 no.2
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• pp.214-226
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• 2007

Because precipitation is influenced by various atmospheric variables, it is highly nonlinear. Although precipitation predicted by a dynamic model can be corrected by using a nonlinear Artificial Neural Network, this approach has limits such as choices of the initial weight, local minima and the number of neurons, etc. In the present paper, we correct simulated precipitation by using a multiple linear regression (MLR) method, which is simple and widely used. First of all, Ensemble hindcast is conducted by the PNU/CME Coupled General Circulation Model (CGCM) (Park and Ahn, 2004) for the period from April to August in 1979-2005. MLR is applied to precipitation simulated by PNU/CME CGCM for the months of June (lead 2), July (lead 3), August (lead 4) and seasonal mean JJA (from June to August) of the Northeast Asian region including the Korean Peninsula $(110^{\circ}-145^{\circ}E,\;25-55^{\circ}N)$. We build the MLR model using a linear relationship between observed precipitation and the hindcasted results from the PNU/CME CGCM. The predictor variables selected from CGCM are precipitation, 500 hPa vertical velocity, 200 hPa divergence, surface air temperature and others. After performing a leave-oneout cross validation, the results are compared with the PNU/CME CGCM's. The results including Heidke skill scores demonstrate that the MLR corrected results have better forecasts than the direct CGCM result for rainfall.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.59-65
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• 2003

Four kinds of $Si_3N_4$-based ceramic cutting tools with different sintering time were fabricated to investigante the effect of sintering time on the microstructure, mechanical properties, grain sizes and the cutting performance. An endeavor was also made to determine the relation among mechanical property, Brain size and tool life. $Si_3N_4$ home made cutting tool sintered for 1 hour under $1760^{\circ}$ temperature and 25MPa pressure showed the best cutting performance among selected ceramic tools during machining both Bray cast iron and heat treated SCM440. Multiple linear regression model was used to estimate the tool lift from mechanical property, grain size and showed good result. It was also shown that hardness imposed the biggest offect on tool life.

• Journal of KIISE:Software and Applications
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• v.31 no.1
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• pp.54-60
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• 2004

Face recognition is one of the problems to be solved by appearance based matching technique. However, the appearance of face image is very sensitive to variation in illumination. One of the easiest ways for better performance is to collect more training samples acquired under variable lightings but it is not practical in real world. ]:n object recognition, it is desirable to focus on feature extraction or normalization technique rather than focus on classifier. This paper presents a simple approach to normalization of faces subject to directional illumination. This is one of the significant issues that cause error in the face recognition process. The proposed method, ICR(illumination Compensation based on Multiple Linear Regression), is to find the plane that best fits the intensity distribution of the face image using the multiple linear regression, then use this plane to normalize the face image. The advantages of our method are simple and practical. The planar approximation of a face image is mathematically defined by the simple linear model. We provide experimental results to demonstrate the performance of the proposed ICR method on public face databases and our database. The experimental results show a significant improvement of the recognition accuracy.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.9 s.114
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• pp.897-903
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• 2006

The reduction of the vehicle interior noise has been the main interest of noise and vibration harshness (NVH) engineers. The driver's perception on the vehicle noise is affected largely by psychoacoustic characteristic of the noise as well as the SPL. In particular, the heating, ventilation and air conditioning (HVAC) system sound among the vehicle interior noise has been reflected sensitively in psychoacoustics view point. Even though the HVAC noise is not louder than overall noise level, it clearly affects subjective perception to drivers in the way of making to be nervous or annoyed. Therefore, these days a vehicle engineer takes aim at developing sound quality as well as reduction of noise. In this paper, we acquired noises in the HVAC from many vehicles. Through the objective and subjective sound quality (SQ) evaluation with acquiring noises recorded by the vehicle HVAC system, the simple and multiple regression models were obtained for the subjective evaluation 'Pleasant' using the semantic differential method (SDM). The regression procedure also allows you to produce diagnostic statistics to evaluate the regression estimates including appropriation and accuracy. Furthermore, the neural network (NN) model were obtained using three inputs(loudness, sharpness and roughness) of the SQ metrics and one output(subjective 'Pleasant'). Because human's perception is very complex and hard to estimate their pattern, we used NN model. The estimated models were compared with correlations between output indexes of SQ and hearing test results for verification data 'Pleasant'. As a result of application of the SQ indexes, the NN model was shown with the largest correlation of SQ indexes and we found possibilities to predict the SQ metrics.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.10
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• pp.531-541
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• 2020

This study aims to determine the nature of the association between dividend policy and a corporation's financial performance in emerging countries, as well as the main variables that may have an effect on financial performance. The study included 92 industrial and service sector companies listed on the Amman Stock Exchange (ASE) during the period from 2015 to 2019. The study used Panel Data Analysis and cross-sectional time-series data and simple and multiple linear regression models. A multiple regression model was also developed in order to test whether guess factors may have a possible impact on financial performance (such as Dividend Yield, Dividend Pay-out Ratio, Firm Size, Leverage Ratio, Current Ratio). The data was collected from the annual reports and information that was available on the ASE website covering the period from 2015 to 2019. The results detect a strong relation between DY, DPR, and FSIZE variables that explain firm performance. Also leverage ratio is negatively and significantly associated with ROA and AOE. Moreover, no relations were detected between current ratio and financial performance. The study's conclusion is that dividend policy explains a lot of a company's financial performance, meaning that the dividend policy has a statistically significant impact on company financial performance.

• East Asian Journal of Business Economics (EAJBE)
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• v.10 no.2
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• pp.19-30
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• 2022

Purpose - The research aims to explore the ability of the combination of the Technology Acceptance Model (TAM) and Theory of Reasoned Action (TRA) to predict and explain the online shopping intention of Vietnamese consumers. The findings of this study provide empirical results to assess the factors influencing behavioural intention in the E-commerce field. Research design, data, and methodology - The research approach of this study is quantitative. The data was collected from 214 respondents on e-commerce platforms. The collected data will then be analyzed to test the proposed hypothesis in this study. Multiple Regression Analysis and Simple Linear Regression are employed to test the hypothesis. Result - Perceived benefits, Perceived risk reduction, and trust positively influence Attitude toward using Ecommerce. There is a positive relationship between Subjective norms and Behavioural intention to shop online. There is a positive relationship between Attitude toward using E-commerce and Behavioural intention to shop online. Conclusion - This study is based on the Theory of Reasoned Action (TRA) model and the Technology Acceptance Model (TAM) to explore the factors influencing the online shopping intention of Vietnamese consumers. Besides, this paper contributes to the managerial implications for E-commerce managers and policymakers to promote E-commerce among Vietnamese consumers.

• Journal of the Korea Society for Simulation
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• v.8 no.4
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• pp.53-59
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• 1999

This study proposed multiple linear regression models to predict those who can be easily infected simulator sickness(SS) in simulator or virtual reality environment. In this study, SSQ(Simulator Sickness Questionnaire) scores which are recently used for assessing SS, and RSSQ(Revised Simulator Sickness Questionnaire) scores are selected as dependent variables. Also ten dependent variables are used. The results are these models coefficient of determination(max $R^2=0.52$) is improved 18% more than Kolasinski's model($R^2=0.35$), and it became easy to predict with simple data. Accordingly, we can easily predict who will be apt to get into simulator sickness.

• The Korean Journal of Emergency Medical Services
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• v.12 no.3
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• pp.129-135
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• 2008

Purpose : This study was to analyze the demand for emergency ambulance service and to characterize the factors associated with the demand. Method : The basis for the model was from the actual demand for public emergency ambulance and socioeconomic and geographic characteristics. Multiple regression analyses were done for the related characteristics of public ambulance service. Result : The model explained total demand with a high degree of accuracy : the coefficient of determination($R^2=0.96$). For the regression, the set of variables indicative of low socioeconomic status were all significant. It showed the inappropriate use of public ambulance system. Public ambulance demand increased in higher housing density, low income, male unemployment and female labor force. Conclusion : The demand for public ambulances appeared to be highly predictable, using a simple linear model employing socioeconomic variables, quality of service variables, and land use variables. Low-income families tended, to use the public ambulance system more often than higher income. Area having elderly people or children also made many calls. Estimated demand calls were stable and had a tendency to be similar incident types.