• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.521-531
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• 2002

Various diagnostic techniques for identifying influential observations are mostly based on the deletion of a single observation. While such techniques can satisfactorily identify influential observations in many cases, they will not always be successful because of some mask effect. It is necessary, therefore, to develop techniques that examine the potentially influential effects of a subset of observations. The partial regression plots can be used to examine an influential observation for a single parameter in multiple linear regression. However, it is often desirable to detect influential observations for a subset of regression parameters when interest centers on a selected subset of independent variables. Thus, we propose a diagnostic measure which deals with detecting influential observations on a subset of regression parameters. In this paper, we propose a measure M, which can be effectively used for the detection of influential observations on a subset of regression parameters in multiple linear regression. An illustrated example is given to show how we can use the new measure M to identify influential observations on a subset of regression parameters.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.5
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• pp.314-326
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• 2016

The purpose of variable selection techniques is to select a subset of relevant variables for a particular learning algorithm in order to improve the accuracy of prediction model and improve the efficiency of the model. We conduct an empirical analysis to evaluate and compare seven well-known variable selection techniques for multiple linear regression model, which is one of the most commonly used regression model in practice. The variable selection techniques we apply are forward selection, backward elimination, stepwise selection, genetic algorithm (GA), ridge regression, lasso (Least Absolute Shrinkage and Selection Operator) and elastic net. Based on the experiment with 49 regression data sets, it is found that GA resulted in the lowest error rates while lasso most significantly reduces the number of variables. In terms of computational efficiency, forward/backward elimination and lasso requires less time than the other techniques.

• Journal of Korean Society for Quality Management
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• v.43 no.2
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• pp.151-168
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• 2015

Purpose: The purpose of this study was to investigate the effect of the innovation performance through business innovation activities by small and medium size company as well as to test the moderating effects on the innovation techniques'understanding and conformance. Methods: The data for the empirical analysis was collected from 132 companies mainly located in Jeolla and Gyeonggi Province. Cronbach's alpha factor analysis, multiple regression analysis and hierarchical multiple regression were used for statistical methods. Results: The Statistical analysis results show that innovation performance through business innovation techniques and activities was effected by CEO leadership, company capability and training for employees. Conclusion: Through this study we tried to give some suggestions for improving innovation performance through business innovation activities for the small and medium size company.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.05e
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• pp.49-52
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• 2003

The performance of insulators under contaminated conditions is the underlying and the most factor that determines insulation design for outdoor applications, Among the contamination factors, The sea salt is the most dangerous factor, and the salt factor have closed relation with climatic conditions, such as wind, temperature, humidity and so on, Effect of these factors to insulation system is different of each other, and need to show the correlation by multiple regression analysis techniques. In this paper, predicted and analyzed equivalent salt deposit density (ESDD) by change climatic condition through multiple regression analysis.

• Geomechanics and Engineering
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• v.15 no.1
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• pp.631-643
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• 2018

The estimation of the strength and other mechanical parameters characterizing the tensile behavior of granites can play an important role in civil engineering tasks such as design, construction, rehabilitation and repair of existing structures. The purpose of this paper is to apply data mining techniques, such as multiple regression (MR), artificial neural networks (ANN) and support vector machines (SVM) to estimate the mechanical properties of granites. In a first phase, the mechanical parameters defining the complete tensile behavior are estimated based on the tensile strength. In a second phase, the estimation of the mechanical properties is carried out from different combination of the physical properties (ultrasonic pulse velocity, porosity and density). It was observed that the estimation of the mechanical properties can be optimized by combining different physical properties. Besides, it was seen that artificial neural networks and support vector machines performed better than multiple regression model.

• Journal of Korean Society of Rural Planning
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• v.2 no.2
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• pp.103-108
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• 1996

In rural planning, the cost estimation of project is a key factor for planning. Therefore, development of reliable cost estimation method is essential. Recently, new techniques are suggested for determination of project cost using historical cost data. In this study, a multiple-regression analysis was used to determine the cost of the farm land consolidation. The results demonstrated that multiple regression analysis using historical cost data can be applicable to project cost estimation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.12
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• pp.2556-2564
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• 2002

The design of airfoil had practiced by repeat tests in its first stage, though an airfoil has as been designed based on simulations according to techniques of computational fluid dynamics. Here, using of traditional optimization is unsuitable because a state of flux is hypersensitive to the shape of airfoil. Therefore the paper optimized the shape of airfoil in transonic region using a genetic algorithm (GA). Response surfaces are based on back propagation neural network (BPN) and regression model. Training data of BPN and regression model were obtained by computational fluid dynamic analysis using CFD-ACE, and each analysis has been designed by design of experiments.

• Computers and Concrete
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• v.13 no.1
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• pp.17-30
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• 2014

This research presents the effect of various ground pozzolanic materials in blended cement concrete on the strength and chloride penetration resistance. An experimental investigation dealing with concrete incorporating ground fly ash (GFA), ground bottom ash (GBA) and ground rice husk ash (GRHA). The concretes were mixed by replacing each pozzolan to Ordinary Portland cement at levels of 0%, 10%, 20% and 40% by weight of binder. Three different water to cement ratios (0.35, 0.48 and 0.62) were used and type F superplasticizer was added to keep the required slump. Compressive strength and chloride permeability were determined at the ages of 28, 60, and 90 days. Furthermore, using this experimental database, linear and nonlinear multiple regression techniques were developed to construct a mathematical model of chloride permeability in concretes. Experimental results indicated that the incorporation of GFA, GBA and GRHA as a partial cement replacement significantly improved compressive strength and chloride penetration resistance. The chloride penetration of blended concrete continuously decreases with an increase in pozzolan content up to 40% of cement replacement and yields the highest reduction in the chloride permeability. Compressive strength of concretes incorporating with these pozzolans was obviously higher than those of the control concretes at all ages. In addition, the nonlinear technique gives a higher degree of accuracy than the linear regression based on statistical parameters and provides fairly reasonable absolute fraction of variance ($R^2$) of 0.974 and 0.960 for the charge passed and chloride penetration depth, respectively.

• Proceedings of the KSRS Conference
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• v.2
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• pp.635-638
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• 2006

Regression of conventional prediction techniques in data mining uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to time series, the rate of prediction accuracy will be decreased. This paper proposes an incremental regression for time series prediction like typhoon track prediction. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of typhoon track prediction experiment are performed by the proposed technique IMLR(Incremental Multiple Linear Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.26
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• pp.59-66
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• 1992

This Paper, first, tries to optimize the output specifications with uncertain characteristics. And then aims to solve the problem not only by making use of transformed multiple regression equation which can yield objective function of output characteristics but also by formulating developed multiple fuzzy goal programming using fuzzy set theory which can treat uncertainty easily, and the efficiency of these techniques, will be also demonstrated through a case study.