• Journal of the Korean Data and Information Science Society
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• v.12 no.1
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• pp.99-101
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• 2001

In this note, we show that a linear regression model, using entropy and degree of nearness of fuzzy numbers, suggested by Wang and Li[FSS 36, 125-136] seems to be unreasonable by an example.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.953-963
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.

• Communications for Statistical Applications and Methods
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• v.14 no.2
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• pp.281-286
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• 2007

Cook's distance is generalized to the multiple linear regression with linear constraints on regression coefficients. It is used for identifying influential observations in constrained regression models. A numerical example is provided for illustration.

• Korean Journal of Food Science and Technology
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• v.51 no.3
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• pp.227-236
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• 2019

Antioxidant activity in water and ethanol extracts of dried Lycium chinense fruit, as a result of the total phenolic and tannin content, was measured using a number of chemical and biochemical assays for radical scavenging and inhibition of lipid peroxidation, with the analysis being extended by applying a bootstrapping statistical method. Previous statistical analyses mostly provided linear correlation and regression analyses between antioxidant activity and increasing concentrations of phenolics and tannins in a concentration-dependent mode. The present study showed that multiple component or multivariate analysis by applying multiple regression analysis or regression planes proved more informative than linear regression analysis of the relationship between the concentration of individual components and antioxidant activity. In this paper, we represented the multivariate analysis of antioxidant activities of both phenolic and tannin contents combined in the water and ethanol extracts, which revealed the hidden observations that were not evident from linear statistical analysis.

• Journal of Korean Society for Quality Management
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• v.21 no.1
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• pp.161-170
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• 1993

This study attempts to present the linear regression analysis that involves more than one regressor variable, because regression analysis is the most widely used statistical technique for describing, predicting and estimating the relationships between given data. The model of multiple linear regression may be solved directly by the two linear programming methods, i.e., to minimize the sum of the absolute deviation (MSD) and to minimize the maximum deviation(MMD). In addition, some results was compared to each techniques for accuracy and tested to the validity of statistical meaning.

• Archives of Pharmacal Research
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• v.11 no.2
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• pp.99-113
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• 1988

Quantitation of ethenzamide, isopropylantipyrine and caffeine takes about 41 hrs by conventional GC method. Quantitation of allylisoprorylacetylurea takes about 40 hrs by conventional UV method. But quantitation of them takes about 6 hrs by DRIFT developing method. Each standard and sample sieved, powdered and acquired DRIFT spectrum. Out of them peak of each component was selected and ratio of each peak to standard peak was acquired, and then linear stepwise multiple regression was performed with these data and concentration. Reflectance value, Kubelka-Munk equation and Inverse-Kubelka-Munk equation were modified by us. Inverse-Kubelka-Munk equation completed the deficit of Kubelka-Munk equation. Correlation coefficients acquired by conventioanl GC and UV against DRIFT were more than 0.95.

• Journal of the Korean GEO-environmental Society
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• v.11 no.12
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• pp.63-74
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• 2010

Normally consolidated and slightly overconsolidated soft clay layer is widely distributed in the south coast of Korea. To ensure the efficient and economical construction design of any structure to be built on this soft soil, exhaustive studies related to geotechnical and physical engineering properties are required. In this study, the relationship of the physical properties of southern Gwangyang marine clay in the Korea Peninsula were examined, including natural water content, specific gravity, total unit weight, initial void ratio, liquid limit, plastic limit, and physical properties of activity and soil parameters. For the parameter relationship analysis, the latest relatively reliable data on the large harbor construction work were used, optimum values were deducted with linear regression and non-linear regression between soil parameters, water content or initial void ratio appears to be very large. Moreover, in the linear and involution pattern regression, equal coefficient of determination appeared. The relationship of the different parameters was shown to be excellent in the non-linear regression of involution equation and exponential equation pattern compared with the findings of linear regression analysis.

• Journal of the Korean GEO-environmental Society
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• v.9 no.4
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• pp.37-45
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• 2008

Normally consolidated and slightly overconsolidated soft clay layer is widely distributed in the south coast of Korea. To ensure the efficient and economical construction design of any structure to be built on this soft soil, exhaustive studies are required related to geotechnical engineering properties. In this study, the relationship of the physical properties of southern marine clay in the Korea Peninsula were examined, including natural water content, specific gravity, total unit weight, initial void ratio, liquid limit, plastic limit, and physical properties of activity and soil parameters. For the parameter relationship analysis, the latest relatively reliable data on the large harbor construction work were used, optimum values were deducted with linear regression and non-linear regression between soil parameters, water content or initial void ratio appears to be very large. Moreover, in the linear and involution pattern regression, equal coefficient of determination appeared. The relationship of the different parameters was shown to be excellent in the non-linear regression of involution equation and exponential equation pattern compared with the findings of linear regression analysis.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1902-1908
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• 2022

In many of the complex systems modeled in the field of nuclear engineering, it is often useful to use linear regression-based analyses to analyze relationships between model parameters and responses of interests. In cases where the response of interest is calculated by a simulation which uses Monte Carlo methods, there will be some uncertainty in the responses. Further, the reduction of this uncertainty increases the time necessary to run each calculation. This paper presents some discussion on how the Monte Carlo error in the response of interest influences the error in computed linear regression coefficients. A mathematical justification is given that shows that when performing linear regression in these scenarios, the error in regression coefficients can be largely independent of the Monte Carlo error in each individual calculation. This condition is only true if the total number of calculations are scaled to have a constant total time, or amount of work, for all calculations. An application with a simple pin cell model is used to demonstrate these observations in a practical problem.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.1043-1052
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• 2009

To derive easily the coefficient of permeability from several other soil properties, the estimation model of coefficient of permeability was proposed using linear regression analysis. The coefficient of permeability is one of the major factors to evaluate the soil characteristics. The study area is located in Kangwon-do Pyeongchang-gun Jinbu-Myeon. Soil samples of 45 spots were taken from the study area and various soil tests were carried out in laboratory. After selecting the soil factor influenced by the coefficient of permeability through the correlation analysis, the estimation model of coefficient of permeability was developed using the linear regression analysis between the selected soil factor and the coefficient of permeability from permeability test. Also, the estimation model of coefficient of permeability was compared with the results from permeability test and empirical equation, and the suitability of proposed model was proved. As the result of correlation analysis between various soil factors and the coefficient of permeability using SPSS(statistical package for the social sciences), the largest influence factor of coefficient of permeability were the effective grain size, porosity and dry unit weight. The coefficient of permeability calculated from the proposed model was similar to that resulted from permeability test. Therefore, the proposed model can be used in case of estimating the coefficient of permeability at the same soil condition like study area.