• Journal of Korea Water Resources Association
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• v.41 no.3
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• pp.325-340
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• 2008

This study employs Bayesian multiple regression analysis using the ordinary least squares method for regional low flow frequency analysis. The parameter estimates using the Bayesian multiple regression analysis were compared to conventional analysis using the t-distribution. In these comparisons, the mean values from the t-distribution and the Bayesian analysis at each return period are not significantly different. However, the difference between upper and lower limits is remarkably reduced using the Bayesian multiple regression. Therefore, from the point of view of uncertainty analysis, Bayesian multiple regression analysis is more attractive than the conventional method based on a t-distribution because the low flow sample size at the site of interest is typically insufficient to perform low flow frequency analysis. Also, we performed low flow prediction, including confidence interval, at two ungauged catchments in the Nakdong River basin using the developed Bayesian multiple regression model. The Bayesian prediction proves effective to infer the low flow characteristic at the ungauged catchment.

• International Journal of Highway Engineering
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• v.19 no.5
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• pp.49-58
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• 2017

PURPOSES : The objective of this study is to develop a simple regression model in designing the asphalt concrete (AC) overlay thickness using the Mechanistic-empirical pavement design guide (MEPDG) program. METHODS : To establish the AC overlay design equation, multiple regression analyses were performed based on the synthetic database for AC thickness design, which was generated using the MEPDG program. The climate in Seoul city, a modified Hirsh model for determining dynamic modulus of asphalt material, and a new damaged master curve approach were used in this study. Meanwhile, the proposed rutting model developed in Seoul city was then used to calibrate the rutting model in the MEPDG program. The AC overlay design equation is a function of the total AC thickness, the ratio of AC overlay thickness and existing AC thickness, the ratio of existing AC modulus and AC overlay modulus, the subgrade condition, and the annual average daily truck traffic (AADTT). RESULTS : The regression model was verified by comparing the predicted AC thickness, the AADTT from the model and the MEPDG. The regression model shows a correlation coefficient of 0.98 in determining the AC thickness and 0.97 in determining AADTT. In addition, the data in Seoul city was used to validate the regression model. The result shows that correlation coefficient between the predicted and measured AADTT is 0.64. This indicates that the current model is more accuracy than the previous study which showed a correlation coefficient of 0.427. CONCLUSIONS:The high correlation coefficient values indicate that the regression equations can predict the AC thickness accurately.

• Communications for Statistical Applications and Methods
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• v.5 no.1
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• pp.77-84
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• 1998

ARES plot (Cook and Weisberg, 1987) idea is applied to a multiple regression model in which the relation between a response variable and some independent variable is nonlinear. This method is expected to show the impact on the function to which and independent variable should be transformed, as a variable is smoothly added to the model.

• Korean Journal of Construction Engineering and Management
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• v.13 no.2
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• pp.91-101
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• 2012

In planning phase of Public Multi-Housing Projects, it is required to develop the methodology and criteria for fair cost prediction with influencing power from planning phase to occupancy phase. Many studies still have focused on the prediction of cost by multiple regression. However, there is no logical explanation about the influence of nonmetric variables for the prediction of cost in planning phase. Accordingly, this research pursues a cost prediction model including nonmetric variables for use in planning phase. There are 3 steps of this research : 1) Finding the factors influencing construction cost and assigning variables for a multiple regression. 2) Conducting a dummy regression analysis with nonmetric variables and model validation by comparing actual cost data. 3) Developing the ratio of RC structure cost to wall structure cost by using cost predection model. The results could establish cost prediction process including the influence of nonmetric variables and the ratio of RC structure cost to wall structure cost.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.4
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• pp.33-42
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• 2019

In this study, the channel and floodplain widths are indirectly measured for three different watersheds using satellite images to reflect the shape of Korean channels in the Soil and Water Assessment Tool (SWAT) model. For measuring the channel and floodplain widths, multiple satellite images were referred to ensure the widest width of certain points. In the single channel, the widths at the multiple points were measured. Based on the measured data, the regression equations were derived to estimate the channel and floodplain widths according to watershed areas. Applying these developed equations, this study evaluated the effect of the change of channel and floodplain widths on the SWAT simulation by comparing to the measured streamflow data. The developed equations estimated larger channel width and smaller floodplain compared with those calculated in the current SWAT model. As shown in the results, there was no considerable changes in the predicted streamflow using the current and developed equations. However, the flow velocity and channel depth calculated from the developed equations were smaller than those of the current equations. The differences were caused by the effect of different channel geometries used for calculating the hydraulic characteristics. The channel geometries also affected the water quality simulation in channels because the hydraulic characteristics calculated by the channel geometries are directly related to the water quality simulation. Therefore, application of the river cross-sectional regression equation reflecting the domestic stream shape is necessary for accurate water quantity / quality and water ecosystem simulation using hydrological model.

• Earthquakes and Structures
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• v.25 no.6
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• pp.429-442
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• 2023

To study the seismic vulnerability of the composite material structure of adobe and timber, we collected and statistically analysed empirical observation samples of 542,214,937 m² and 467,177 buildings that were significantly impacted during the 179 earthquakes that occurred in mainland China from 1976 to 2010. In multi-intensity regions, combined with numerical analysis and a probability model, a non-linear continuous regression model of the vulnerability, considering the empirical seismic damage area (number of buildings) and the ratio of seismic damage, was established. Moreover, a probability matrix model of the empirical seismic damage mean value was provided. Considering the coupling effect of the annual and seismic fortification factors, an empirical seismic vulnerability curve model was constructed in the multiple-intensity regions. A probability matrix model of the mean vulnerability index (MVI) was proposed, and was validated through the above-mentioned reconnaissance sample data. A matrix model of the MVI of the regions (19 provinces in mainland China) based on the parameter (MVI) was established.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.4
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• pp.23-29
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• 2004

In this study, four kinds of $Si_3N_4$-based ceramic cutting tools with different sintering time were fabricated to investigate the relation among mechanical properties, grain size and tool life. They were used to turn gray cast iron at a cutting speed of 330m/min and depth of cut of 0.5mm and 1mm in dry, continuos cutting conditions. Multiple linear regression model was used to determine the relations among the mechanical property, grain size and the density. It was found that the combination of hardness and fracture toughness showed a good relation with tool life. It was also shown that hardness was the most important single element for the tool life.

• Journal of Environmental Science International
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• v.10 no.3
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• pp.173-178
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• 2001

The aim of this paper is to evaluate users benefit values of theme park using Travel Cost Method with special reference to Woobang Tower Land in Taegu for the estimation of economic values. This research is mainly based on questionnaire survey of 100 users of the theme park. Socio-economic factors such as income, year of education, annual income, age and money(travel cost) are analysed from 5 residential areas of the respondents. Multiple regression analysis was used for the evaluation of annual number of park visitings based on the analysis. The regression model shows NV = $\alpha$+$\beta_1$TC+$\beta_2$INC+$\beta_3$EDU+$\beta_4$AGE (NV : Annual Number of Visitings, TC : Travel Cost, INC : Annual Income, EDU : Years of Education, AGE : Age). Regarding to visitors demand curve based on the equation showed that annual economic values of Woobang Tower Land was estimated as 50billion Korean Won.

• 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.

• Communications for Statistical Applications and Methods
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• v.26 no.5
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• pp.507-517
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• 2019

Statistical matching is a method of combining multiple sources of data that are extracted or surveyed from the same population. It can be used in situation when variables of interest are not jointly observed. It is a low-cost way to expect high-effects in terms of being able to create synthetic data using existing sources. In this paper, we propose the several statistical micro matching methods using a multinomial logistic regression model when all variables of interest are categorical or categorized ones, which is common in sample survey. Under conditional independence assumption (CIA), a mixed statistical matching method, which is useful when auxiliary information is not available, is proposed. We also propose a statistical matching method with auxiliary information that reduces the bias of the conventional matching methods suggested under CIA. Through a simulation study, proposed micro matching methods and conventional ones are compared. Simulation study shows that suggested matching methods outperform the existing ones especially when CIA does not hold.