• Korean Journal of Applied Biomechanics
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• v.26 no.4
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• pp.353-360
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• 2016

Objective: The aim of this study was to investigate the relationship between aiming patterns and scores in archery shooting. Method: Four (N = 4) elementary-level archers from middle school participated in this study. Aiming pattern was defined by averaged acceleration data measured from accelerometers attached on the body during the aiming phase in archery shooting. Stepwise multiple regression analysis was used to test whether a model incorporating aiming patterns from all nine accelerometers could predict the scores. In order to extract period of interest (POI) data from raw data, a Dynamic Time Warping (DTW)-based extraction method was presented. Results: Regression models for all four subjects are conducted with different significance levels and variables. The significance levels of the regression models are 0.12%, 1.61%, 0.55%, and 0.4% respectively; the $R^2$ of the regression models is 64.04%, 27.93%, 72.02%, and 45.62% respectively; and the maximum significance levels of parameters in the regression models are 1.26%, 4.58%, 5.1%, and 4.98% respectively. Conclusion: Our results indicated that the relationship between aiming patterns and scores was described by a regression model. Analysis of the significance levels, variables, and parameters of the regression model showed that our approach - regression analysis with DTW - is an effective way to raise scores in archery shooting.

• Proceedings of the KSR Conference
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• 1999.11a
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• pp.566-573
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• 1999

This paper is the result of sensitivity analysis of derailment with respect to the selected suspension elements for the rail vehicle. Derailment phenominon has been explained by the derailment quotient. Thus, the sensitivity of derailment is suggested by a response surface model(RSM) which is a functional relationship between derailment quotient and characteristics of suspension elements. To summarize generation of RSM, we can introduce the procedure of sensitivity analysis as follows. First, to form a RSM, a experiment is performed by a dynamic analysis code, VAMPIRE according to a kind of the design of experiments(DOE). Second, RSM is constructed to a 1$\^$st/ order polynomial and then main effect fators are screened through the stepwise regression. Finally, we can see the sensitivity level through the RSM which only consists of the main effect factors and is expressed by the liner, interaction and quadratic effect terms.

• Korean Journal of Community Nutrition
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• v.3 no.5
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• pp.679-684
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• 1998

This study was conducted to investigate the determinants of obesity during adolescnece. A total of 726 adolescents living in rural areas in Korea had been observed for four years from 1992 to 1996 regarding their diet, sexual maturity, blood profile and physical growth. Stepwise multiple regression analysis was used to identify priorities fo the importance between the factors influencing obesity. The average nutrient intake over the three year period was higher than that of the Korean Recommended Dietary Allowances. The prevalence of obesity for the subjects based on BMI was 9.5%. Results of the stepwise multiple regression analysis showed that blood components and sexual maturity were more significant factors for determining the obesity than the dietary factors. The result may suggest that to understand obesity in children it is necessary to develop on analytical model for the children rather than using the existing analytical model developed mostly for adult patients of obesity. The model should include a wide range of variables such as diet, sexual maturity and changes in blood.

• Journal of Korean Academy of Nursing
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• v.33 no.5
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• pp.625-632
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• 2003

Purpose: The purpose of this study was to assess the importance and sensitivity to nursing interventions of four nursing sensitive nursing outcomes selected from the Nursing Outcomes Classification (NOC). Outcomes for this study were 'Knowledge: Diet', 'Knowledge: Disease Process', 'Knowledge: Energy Conservation', and 'Knowledge: Health Behaviors'. Method: Data were collected from 183 nurses working in 2 university hospitals. Fehring method was used to estimate outcome and indicators' content and sensitivity validity. Multiple and stepwise regression were used to evaluate relationships between each outcome and its indicators. Result: Results confirmed the importance and nursing sensitivity of outcomes and their indicators. Key indicators of each outcomes were found by multiple regression. 'Knowledge: Diet' was suggested for adding new indicators because the variance explained by indicators was relatively low. Not all of the indicators selected for stepwise regression model were rated for highly in Fehring method. The R² statistics of the stepwise regression models were between 18 and 63% in importance by selected indicators and between 34 and 68% in contribution by selected indicators. Conclusion: This study refined what outcomes and indicators will be useful in clinical practice. Further research will be required for the revision of outcome and indicators of NOC. However, this study refined what outcomes and indicators will be useful in clinical practice.

• Journal of Microbiology and Biotechnology
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• v.10 no.4
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• pp.476-481
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• 2000

A linear correlation, by the stepwise multiple regression analysis, was found between the sensory test of Sigumjang aroma and the gas chromatographic data which were transformed with logarithm. GC data is the most objective method to evaluate Sigumjang aroma. A multiple correlation coefficient and a determination coefficient of more than 0.9 were obtained at the 9th and 13th steps, respectively. At step 31, the coefficient of determination level of 0.95 was attained. The accuracy of its estimation became higher as the number of the variables entered into the regression model increased. Over 90% of the Sigumjang aroma was explained by 13 compounds indentified on GC. The contributing proportion of the peak 26 was the highest followed by peaks 57 (9.27%), 29 (7.51%), 54 (6.01%), 8 (5.99%), 49 (4.97%), and 13 (4.11%).

• Journal of Environmental Science International
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• v.16 no.11
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• pp.1301-1312
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• 2007

The objective of this study is to propose a critical storm duration forecasting model on storm runoff in small river basin. The critical storm duration data of 582 sub-basin which introduced disaster impact assessment report on the National Emergency Management Agency during the period from 2004 to 2007 were collected, analyzed and studied. The stepwise multiple regression method are used to establish critical storm duration forecasting models(Linear and exponential type). The results of multiple regression analysis discriminated the linear type more than exponential type. The results of multiple linear regression analysis between the critical storm duration and 5 basin characteristics parameters such as basin area, main stream length, average slope of main stream, shape factor and CN showed more than 0.75 of correlation in terms of the multi correlation coefficient.

• Communications for Statistical Applications and Methods
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• v.28 no.2
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• pp.189-204
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• 2021

In a regression analysis, a single best model is usually selected among several candidate models. However, it is often useful to combine several candidate models to achieve better performance, especially, in the prediction viewpoint. Model combining methods such as stacking and Bayesian model averaging (BMA) have been suggested from the perspective of averaging candidate models. When the candidate models include a true model, it is expected that BMA generally gives better performance than stacking. On the other hand, when candidate models do not include the true model, it is known that stacking outperforms BMA. Since stacking and BMA approaches have different properties, it is difficult to determine which method is more appropriate under other situations. In particular, it is not easy to find research papers that compare stacking and BMA when regression model assumptions are violated. Therefore, in the paper, we compare the performance among model averaging methods as well as a single best model in the linear regression analysis when standard linear regression assumptions are violated. Simulations were conducted to compare model averaging methods with the linear regression when data include outliers and data do not include them. We also compared them when data include errors from a non-normal distribution. The model averaging methods were applied to the water pollution data, which have a strong multicollinearity among variables. Simulation studies showed that the stacking method tends to give better performance than BMA or standard linear regression analysis (including the stepwise selection method) in the sense of risks (see (3.1)) or prediction error (see (3.2)) when typical linear regression assumptions are violated.

• Journal of Navigation and Port Research
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• v.42 no.6
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• pp.539-545
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• 2018

In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.

• IE interfaces
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• v.23 no.1
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• pp.78-88
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• 2010

In this paper we develop a model for the performance evaluation of defense R&D test development projects based on analytic hierarchy process. First, evaluation indicators are collected through the related literature survey and a delphi inquiry method. Second, stepwise multiple linear regression is used for developing a hierarchical structure for analytic hierarchy process in the evaluation model, which can make the selected evaluation indicators of the hierarchical structure independent. Also we verify the effectiveness of proposed indicators of the performance evaluation by comparing with the existing evaluation indicators. The developed indicators for the performance evaluation is more reasonable and practical than the previous indicators on defense R&D test development projects.

• Advances in Computational Design
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• v.7 no.2
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• pp.113-128
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• 2022

Water consumption is strongly affected by numerous factors, such as population, climatic, geographic, and socio-economic factors. Therefore, the implementation of a reliable predictive model of water consumption pattern is challenging task. This study investigates the performance of predictive models based on multi-layer perceptron (MLP), multiple linear regression (MLR), and support vector regression (SVR). To understand the significant factors affecting water consumption, the stepwise regression (SW) procedure is used in MLR to obtain suitable variables. Then, this study also implements three predictive models based on these significant variables (e.g., SWMLR, SWMLP, and SWSVR). Annual data of water consumption in Thailand during 2006 - 2015 were compiled and categorized by provinces and distributors. By comparing the predictive performance of models with all variables, the results demonstrate that the MLP models outperformed the MLR and SVR models. As compared to the models with selected variables, the predictive capability of SWMLP was superior to SWMLR and SWSVR. Therefore, the SWMLP still provided satisfactory results with the minimum number of explanatory variables which in turn reduced the computation time and other resources required while performing the predictive task. It can be concluded that the MLP exhibited the best result and can be utilized as a reliable water demand predictive model for both of all variables and selected variables cases. These findings support important implications and serve as a feasible water consumption predictive model and can be used for water resources management to produce sufficient tap water to meet the demand in each province of Thailand.