• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.19 no.11
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• pp.761-769
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• 2007

The purpose of this study is to present a simplified model of predicted mean vote (PMV) using multiple regression analysis. We performed the experiments and the numerical calculations in the lecture room during summer and winter to simplify PMV. And the multiple regression analysis on PMV was conducted to estimate the contribution of independent variables toward PMV. From the multiple regression analysis, we found that the effect of independent variables on PMV followed in order, clo value>air temperatur>air velocity>mean radiant temperature>relative humidity. And the simplified PMV was proposed through a few assumptions and then was compared with original PMV. They had a good agreement with each other. Additionally, we compared the simplified PMV with EDT. We expected that the simplified PMV can be more useful than EDT to evaluate the thermal comfort in the place, where radiation is dominant. But the comfort range of the simplified PMV should be adjusted to predict the exact thermal comfort in the future.

• Current Research on Agriculture and Life Sciences
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• v.24
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• pp.49-53
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• 2006

The objective of this study was to present regression equations between reservoir water quality and land use types of the watersheds. In order to derive regression equations, a multiple linear regression analysis was used using observed data from 88 reservoirs in the Kyungpook Provcince. The measured values of BOD, COD, T-N, and T-P were correlated with the areas of land use types. 23 regression equations were obtained for all the water quality items and watershed sizes. The results showed that 2 regression equations have the multiple correlation coefficient(MCC) above 0.90, 10 regression equations have the MCC values from 0.70 to 0.90, 9 equations have the MCC from 0.40 to 0.70, and 2 equations have the MCC from 0.20 to 0.40. The results of this study can be used to estimate reservoir water quality simply and quickly in the planning phase.

• 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 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.

• 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.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.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.325-336
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• 2019

In this article we extend the discrete Weibull regression model in the presence of missing data. Discrete Weibull regression models can be adapted to various type of dispersion data however, it is not widely used. Recently Yoo (Journal of the Korean Data and Information Science Society, 30, 11-22, 2019) adapted the discrete Weibull regression model using single imputation. We extend their studies by using multiple imputation also with several various settings and compare the results. The purpose of this study is to address the merit of using multiple imputation in the presence of missing data in discrete count data. We analyzed the seventh Korean National Health and Nutrition Examination Survey (KNHANES VII), from 2016 to assess the factors influencing the variable, 1 month hospital stay, and we compared the results using discrete Weibull regression model with those of Poisson, negative Binomial and zero-inflated Poisson regression models, which are widely used in count data analyses. The results showed that the discrete Weibull regression model using multiple imputation provided the best fit. We also performed simulation studies to show the accuracy of the discrete Weibull regression using multiple imputation given both under- and over-dispersed distribution, as well as varying missing rates and sample size. Sensitivity analysis showed the influence of mis-specification and the robustness of the discrete Weibull model. Using imputation with discrete Weibull regression to analyze discrete data will increase explanatory power and is widely applicable to various types of dispersion data with a unified model.

• Food Science and Biotechnology
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• v.14 no.2
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• pp.249-254
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• 2005

The relationship between Sigumjang gas chromatographic patterns precisely analyzed with capillary column and ranked order in sensory analysis was investigated by stepwise multiple regression analysis. Highly predictable multiple regression models were obtained in the analysis. Ninety percent of the Sigumjang aroma was explained by the regression models at step 15 in four transformation except for absolute value transformed with root square and relative value transformed with logarithm. The aroma of Sigumjang was most affected by 2,3-dimethylpyrazine at absolute value and absolute value transformed with logarithm and by 2-furancarboxaldehyde in other transformation. The quality of sigumjang was highly affected by ${\beta}$-phallendrenal, methylpyrazine, tetramethylpyrazine, 5-methyl-2-furancarboxaldehyde, unknown 2, octanoic acid, 4-ethylphenol, methyl 10,13-octadecanoate and ethyl linoleate.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.7
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• pp.923-927
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• 2008

The objective of this study was to compare random regression model and multiple trait animal model estimates of the (co) variance of total sperm cells over the active lifetime of AI boars. Data were provided by Smithfield Premium Genetics (Rose Hill, NC). Total number of records and animals for the random regression model were 19,629 and 1,736, respectively. Data for multiple trait animal model analyses were edited to include only records produced at 9, 12, 15, 18, 21, 24, and 27 months of age. For the multiple trait method estimates of genetic and residual variance for total sperm cells were heterogeneous among age classifications. When comparing multiple trait method to random regression, heritability estimates were similar except for total sperm cells at 24 months of age. The multiple trait method also resulted in higher estimates of heritability of total sperm cells at every age when compared to random regression results. Random regression analysis provided more detail with regard to changes of variance components with age. Random regression methods are the most appropriate to analyze semen traits as they are longitudinal data measured over the lifetime of boars.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4189-4200
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• 2021

In this study, the relationship between the environmental radiation dose rate and meteorological variables was investigated with multiple regression analysis and big data of those variables. The environmental radiation dose rate and 36 different meteorological variables were measured on Ulleung Island, Republic of Korea, from 2011 to 2015. Not all meteorological variables were used in the regression analysis because the different meteorological variables significantly affect the environmental radiation dose rate during different periods, and the degree of influence changes with time. By applying the Pearson correlation analysis and stepwise selection methods to the big dataset, the major meteorological variables influencing the environmental radiation dose rate were identified, which were then used as the independent variables for the regression model. Subsequently, multiple regression models for the monthly datasets and dataset of the entire period were developed.