• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.35-46
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• 2008

The purposes of this study are to analyze the characteristics and to develop the models of traffic accidents. In pursuing the above, this study gives particular attentions to developing the models(multiple linear, poisson and negative binomial regression) using the data of Cheongju and Cheongwon signalized intersections. The main results analyzed are as follows. First, the accident characteristics of rural area were defined by factor. Second, 4 accident models which are all statistically significant were developed. Finally, such the variables as $X_2$ and $X_{11}$ were evaluated to be specific variables which reflect the characteristics of rural area.

• Journal of the Korean Society of Tobacco Science
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• v.21 no.1
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• pp.36-48
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• 1999

Field test was conducted in Chungbuk province to evaluate the soil fertility using landscape and soil attributes by application of geographic information system(GIS) in 48 tobacco fields during 2 years(1996 ; 23 fields, 1997 ; 25 fields). The soil fertility factors and fertilizer effects were estimated by twenty five independent variables including 13 chemical properties and 12 GIS databases. Twenty five independent variables were classified by two groups, 15 quantitative indexes and 10 qualitative indexes and were analyzed by multiple linear regression (MLR) of SAS, REG and GLM models. The estimation model for evaluation of soil fertility and fertilizer effect was made by giving the estimate coefficient for each quantitative index and for each group of qualitative index significantly selected by MLR. Estimation for soil fertility factors and fertilizer effects by independent variables was better by MLR than single regression showing gradually improvement by adding chemical properties, quantitative indexes and qualitative indexes of GIS. Consequently, it is assumed that this approach by MLR with quantitative and qualitative indexes was available as an evaluation model of soil fertility and recommendation of optimum fertilization for tobacco field.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.31
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• pp.1-10
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• 1994

The purpose of this study is to search for the most effective forecasting model for condenser with independent demand among the quantitative methods such as Brown's exponential smoothing method, Box-Jenkins method, and multiple regression analysis method. The criterion for the comparison of the above models is mean squared error(MSE). The fitting results of these three methods are as follows. 1) Brown's exponential smoothing method is the simplest one, which means the method is easy to understand compared to others. But the precision is inferior to other ones. 2) Box-Jenkins method requires much historic data and takes time to get to the final model, although the precision is superior to that of Brown's exponential smoothing method. 3) Regression method explains the correlation between parts with similiar demand pattern, and the precision is the best out of three methods. Therefore, it is suggested that the multiple regression method is fairly good in precision for forecasting our item and that the method is easily applicable to practice.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.403-411
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• 2021

This study discusses the influence of economic factors on the clothing exports from China and 15 South and Southeast Asian countries to the United States. A basic gravity trade model with three predictors, including the GDP value produced by exporting and importing countries and their geographical distance was established to explain the bilateral trade patterns. The conventional approach of multiple regression and the novel approach of Artificial Neural Networks (ANNs) were developed based on the value of clothing exports from 2012 to 2018 and applied to the trade pattern prediction of 2019. The results showed that ANNs can achieve a more accurate prediction in bilateral trade patterns than the commonly-used econometric analysis of the basic gravity trade model. Future studies can examine the predictive power of ANNs on an extended gravity model of trade that includes explanatory variables in social and environmental areas, such as policy, initiative, agreement, and infrastructure for trade facilitation, which are crucial for policymaking and managerial consideration. More research should be conducted for the examination of the balance between developing countries' economic growth and their social and environmental sustainability and for the application of more advanced machine-learning algorithms of global trade flow examination.

• International Journal of Korean Welding Society
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• v.1 no.2
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• pp.12-17
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• 2001

With the advance of the robotic welding process, procedure optimization that selects the welding procedure and predicts bead width that will be deposited is increased. A major concern involving procedure optimization should define a welding procedure that can be shown to be the best with respect to some standard and chosen combination of process parameters, which give an acceptable balance between production rate and the scope of defects for a given situation. This paper presents a new algorithm to establish a mathematical model f3r predicting bead width through a neural network and multiple regression methods, to understand relationships between process parameters and bead width, and to predict process parameters on bead width for GMA welding process. Using a series of robotic arc welding, additional multi-pass butt welds were carried out in order to verify the performance of the neural network estimator and multiple regression methods as well as to select the most suitable model. The results show that not only the proposed models can predict the bead width with reasonable accuracy and guarantee the uniform weld quality, but also a neural network model could be better than the empirical models.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.594-609
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• 2023

TBM (Tunnel Boring Machine) method is gaining popularity in urban and underwater tunneling projects due to its ability to ensure excavation face stability and minimize environmental impact. Among the prominent models for predicting disc cutter life, the NTNU model uses the Cutter Life Index(CLI) as a key parameter, but the complexity of testing procedures and rarity of equipment make measurement challenging. In this study, CLI was predicted using multiple linear regression analysis and tree-based machine learning techniques, utilizing rock properties. Through literature review, a database including rock uniaxial compressive strength, Brazilian tensile strength, equivalent quartz content, and Cerchar abrasivity index was built, and derived variables were added. The multiple linear regression analysis selected input variables based on statistical significance and multicollinearity, while the machine learning prediction model chose variables based on their importance. Dividing the data into 80% for training and 20% for testing, a comparative analysis of the predictive performance was conducted, and XGBoost was identified as the optimal model. The validity of the multiple linear regression and XGBoost models derived in this study was confirmed by comparing their predictive performance with prior research.

• Water Engineering Research
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• v.4 no.2
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• pp.59-68
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• 2003

The applicabilities and validities of two methodologies fur the prediction of THM (trihalomethane) formation in a water pipeline system were proposed and discussed. One is the multiple regression technique and the other is an artificial neural network technique. There are many factors which influence water quality, especially THMs formations in water pipeline systems. In this study, the prediction models of THM formation in water pipeline systems are developed based on the independent variables proposed by American Water Works Association(AWWA). Multiple linear/nonlinear regression models are estimated and three layer feed-forward artificial neural networks have been used to predict the THM formation in a water pipeline system. Input parameters of the models consist of organic compounds measured in water pipeline systems such as TOC, DOC and UV254. Also, the reaction time to each measuring site along pipeline is used as input parameter calculated by a hydraulic analysis. Using these variables as model parameters, four models are developed. And the predicted results from the four developed models are compared statistically to the measured THMs data set. It is shown that the artificial neural network approaches are much superior to the conventional regression approaches and that the developed models by neural network can be used more efficiently and reproduce more accurately the THMs formation in water pipeline systems, than the conventional regression methods proposed by AWWA.

• Research in Community and Public Health Nursing
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• v.13 no.3
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• pp.523-531
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• 2002

Purpose: The purpose of this study was to assess the importance and sensitivity to nursing interventions of six sensitive nursing outcomes selected from the Nursing Outcomes Classification. The outcomes in this study were Self-Care: Activities of Daily Living, Self-Care: Instrumental Activities of Daily Living, Treatment Behavior: Illness or Injury, Knowledge: Health Promotion, Caregiver Performance: Direct Care, and Caregiver Physical Health. Method: Data were collected from 97 visiting nurses working in public health centers located in a province and a capital city. The Fehring method was used to estimate outcomes and indicators for content validity. Simultaneous multiple regression and stepwise regression were used to evaluate relationships between each outcome and its indicators. Results: Results confirmed the importance and nursing sensitivity of the outcomes and their indicators. Multiple regression revealed key indicators of each outcome. Self-Care: Instrumental Activity of Daily Living needed to be revised. Neither all of the indicators nor the indicators showing the highest importance and contribution ratio were selected as independent variables for the stepwise regression model. The R2 of the regression models ranged from 29 to 56% in importance by selected indicators and from 56 to 83% in contribution. Conclusion: Further research is needed for the revision of outcomes and their indicators.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.10
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• pp.1542-1549
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• 2018

Objective: Because lactation periods in dairy cows lengthen with increasing total milk production, it is important to predict individual productivities after 305 days in milk (DIM) to determine the optimal lactation period. We therefore examined whether the random regression (RR) coefficient from 306 to 450 DIM (M2) can be predicted from those during the first 305 DIM (M1) by using a RR model. Methods: We analyzed test-day milk records from 85,690 Holstein cows in their first lactations and 131,727 cows in their later (second to fifth) lactations. Data in M1 and M2 were analyzed separately by using different single-trait RR animal models. We then performed a multiple regression analysis of the RR coefficients of M2 on those of M1 during the first and later lactations. Results: The first-order Legendre polynomials were practical covariates of RR for the milk yields of M2. All RR coefficients for the additive genetic (AG) effect and the intercept for the permanent environmental (PE) effect of M2 had moderate to strong correlations with the intercept for the AG effect of M1. The coefficients of determination for multiple regression of the combined intercepts for the AG and PE effects of M2 on the coefficients for the AG effect of M1 were moderate to high. The daily milk yields of M2 predicted by using the RR coefficients for the AG effect of M1 were highly correlated with those obtained by using the coefficients of M2. Conclusion: Milk production after 305 DIM can be predicted by using the RR coefficient estimates of the AG effect during the first 305 DIM.

• The Journal of the Korean dental association
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• v.51 no.9
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• pp.511-517
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• 2013

To assess the association between oral health and general health, this study examined the relationship between chewing difficulty and twelve chronic health conditions such as hypertension, hyperlipidemia, diabetes, cerebro- and cardiovascular disease, musculoskeletal disease, respiratory disease, eye/nose/throat disease, stomach/intestinal ulcer, renal dysfunction, thyroid disease, depression, and cancer in Korea. The study population was 3,066 elders aged 65 years old and more from the fourth Korean National Health and Nutrition Examination Survey. Chewing difficulty was measured on a 5-point Likert scale. Chronic conditions were assessed by self-reported questionnaire. Confounders were age, gender, education, income, smoking, drinking, and obesity. Chi-square test, general linear model, and multiple logistic regression model were done with complex sampling design. Musculoskeletal disease (adjusted odds ratio=1.33), respiratory disease (adjusted odds ratio=1.52), and cancer (adjusted odds ratio=1.58) were independently associated with chewing difficulty. Multiple chronic conditions with more than 4 chronic disease showed significant association with chewing difficulty (adjusted odds ratio=1.37).