• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.165-172
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• 1998

This paper considers a linear regression model with censored data where each error term follows a multivariate normal distribution. In this paper we consider the diffuse prior distribution for parameters of the linear regression model. With censored data we derive the full conditional densities for parameters of a multiple regression model in order to obtain the marginal posterior densities of the relevant parameters through the Gibbs Sampler, which was proposed by Geman and Geman(1984) and utilized by Gelfand and Smith(1990) with statistical viewpoint.

• Journal of Ecology and Environment
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• v.29 no.4
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• pp.361-364
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• 2006

The objective of the current study was to characterize and apply multiple regression model relating to vegetation values of the plant species over salt marshes. For each salt marsh community, vegetation and soil variables were investigated in the western coast and the southern coast in South Korea. Osmotic potential of soil and $Cl^-$ content of soil as independent variable had positive and negative influences on vegetation values. Multiple regression model showed that vegetation values of 14 coastal plant communities were determined by pH of soil, osmotic potential of soil and sand content. The multiple regression equation may be applied to the explanation of distribution and abundance of plant communities with exiting ordination plots.

• 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.

• Environmental Engineering Research
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• v.15 no.3
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• pp.135-140
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• 2010

With the various urban characteristics of each city, the existing water demand prediction, which uses average liter per capita day, cannot be used to achieve an accurate prediction as it fails to consider several variables. Thus, this study considered social and industrial factors of 164 local cities, in addition to population and other directly influential factors, and used main substance and cluster analyses to develop a more efficient water demand prediction model that considers unique localities of each city. After clustering, a multiple regression model was developed that proved that the $R^2$ value of the inclusive multiple regression model was 0.59; whereas, those of Clusters A and B were 0.62 and 0.74, respectively. Thus, the multiple regression model was considered more reasonable and valid than the inclusive multiple regression model. In summary, the water demand prediction model using principal component and cluster analyses as the standards to classify localities has a better modification coefficient than that of the inclusive multiple regression model, which does not consider localities.

• Archives of Pharmacal Research
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• v.11 no.1
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• pp.45-51
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• 1988

The feature of resolution enhancement by derivative operation is linked to one of the multivariate analysis, which is multiple linear regression with two options, all possible and stepwise regression. Examined samples were synthetic mixtures of 5 vitamins, thiamine mononitrate, riboflavin phosphate, nicotinamide, pyridoxine hydrochloride and ascorbic acid. All components in mixture were quantified with reasonably good accuracy and precision. Whole data processing procedure was accomplished on-line by the development of three computer programs written in APPLESOFT BASIC language.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.129-132
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• 2000

We propose a new method to deal with the optimized auto-tuning for the PID controller which is used to the process -control in various fields. First of all, in this method, initial values of DC motor are determined by the Ziegler-Nichols method. Finally, after studying the parameters of PID controller by input vector of multiple regression analysis, when we give new K, L, T values to multiple regression model, the optimized parameters of PID controller is found by multiple regression analysis program.

• Proceedings of the Safety Management and Science Conference
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• 2012.04a
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• pp.117-137
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• 2012

In the ALC(Autoclaved lightweight concrete) manufacturing process, if the pre-cured semi-cake is removed after proper time is passed, it will be hard to retain the moisture and be easily cracked. Therefore, in this research, we took the research by multiple regression analysis to find relationship between variables for the prediction the hardness that is the control standard of the removal time. We study the relationship between Independent variables such as the V/T(Vibration Time), V/T movement, expansion height, curing time, placing temperature, Rising and C/S ratio and the Dependent variables, the hardness by multiple regression analysis. In this study, first, we calculated regression equation by the regression analysis, then we tried phased regression analysis, best subset regression analysis and residual analysis. At last, we could verify curing time, placing temperature, Rising and C/S ratio influence to the hardness by the estimated regression equation.

• Journal of Industrial Technology
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• v.26 no.A
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• pp.119-127
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• 2006

The purpose of this study is to present quantitatively the influence of variables that had the largest effect on the changes in suspended solids(SS), which would cause turbid water phenomenon, among water quality factors of the non-point pollution source, and then to develop a multiple regression equation of SS and predict the water quality of ungaged watersheds so as to provide basic data to establish efficient management plans for SS which flow in rivers and lakes. To identify the correlation of SS with the amount of rainfall and the state of land use, a simple correlation analysis and a simple regression analysis were conducted respectively. Finally, a multiple regression analysis was conducted to provide that SS were set as dependent variables while the amount of rainfall, paddy fields and dry fields were set as independent variables. As a result, the amount of rainfall had the most significant influence on changes in SS, followed by dry fields and paddy fields. In addition, the multiple regression equation was developed to predict SS in unmeasurable watersheds.

• Journal of Oral Medicine and Pain
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• v.10 no.1
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• pp.73-89
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• 1985

This study comprised 157 extracted teeth, 73 of the teeth originated from mates and 84 from females, the age range was 12-79 years. The correlation coefficient of each Gustafson's criteria in relation to age was carried out. Age estimation were performed on 157 teeth according to the method by Gustafson and by use of multiple regression, as used by Johanson, after evaluating the six criteria of Gustafson by multiple regression computer analysis. Two prediction formulas and standard deviations were compared with each other. The results were as follows : 1. The author found that six Gustafson's criteria had strong correlation with age except root resorption, and correlation coefficients were r = 0.79 (Transparent dentin), r=0.72 (Secondary dentin), r 0.69 (Periodontal change), r=0.63(Attrition), r = 0.39 (Root resorption), respecti vely. 2. The age estimation formula by Gustafson's method was calculated as follows: Y 8.88 + 3.52X r =0.87, r2 = 0.76, SD = 8.18, F = 483.56, P < 0.01 The age estimation formula by multiple regression was calculated as follows: Y 8.57 + 6.37T + 6.37T + 4.63P + 2.70S + 2.40C + 3.08A + 1.34R r= 0.89, r2 = 0.78, SD = 7.82, F = 91.62, P < 0.01, Durbin-Watson Coefficient = 1.09 3. In comparison of two estimation formulas, the formula by multiple regression, the method of Johanson, was found to be slightly more reliable than Gustafson's method. Gustafson's method SD = 8.18, Multiple regression (Johanson's method) SD = 7.82 4. It was reaffirmed that Gustafson's six criteria could be a independent variable in multiple regression analysis.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.2
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• pp.101-111
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• 2012

In the ALC(Autoclaved lightweight concrete) manufacturing process, if the pre-cured semi-cake is removed after proper time is passed, it will be hard to retain the moisture and be easily cracked. Therefore, in this research, we took the research by multiple regression analysis to find relationship between variables for the prediction the hardness that is the control standard of the removal time. We study the relationship between Independent variables such as the V/T(Vibration Time), V/T movement, expansion height, curing time, placing temperature, Rising and C/S ratio and the Dependent variables, the hardness by multiple regression analysis. In this study, first, we calculated regression equation by the regression analysis, then we tried phased regression analysis, best subset regression analysis and residual analysis. At last, we could verify curing time, placing temperature, Rising and C/S ratio influence to the hardness by the estimated regression equation.