• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.05a
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• pp.397-399
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• 2007

This paper presents multiple regression analysis and data update to improve present spatial electric load forecasting algorithm of the DISPLAN. Spatial electric load forecasting considers a local economy, the number of local population and load characteristics. A Case study is performed for Jeon-Ju and analyzes a trend of the spatial load for the future 20 years. The forecasted information can contribute to an asset management of distribution systems.

• Journal of the military operations research society of Korea
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• v.5 no.2
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• pp.27-38
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• 1979

This method explores a method of aggregating the measures of effectiveness of a weapon system from its characteristics. With this method, the constant sum method and multiple regression are used to develop a functional relationship between system effectiveness and system characteristics. As an example, a study of a tank weapon system was${\cdot}$conducted with data from the U.S. Army Armor School. It was concluded that the aggregation method is feasible, and that for the tank system studied, the reciprocals of system characteristics give a good estimating equation for measuring tank system effectiveness.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.9
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• pp.914-921
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• 2001

The objective of this study is to evaluate the design parameters and to develop the cooling and heating load prediction equations of office buildings. The building load calculation simulation was carried out using the DOE-2.1E program. The results of the simulation was used as a data for ANOVA and multiple regression analysis which could develop the load prediction equations.

• Journal of Families and Better Life
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• v.17 no.3
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• pp.131-144
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• 1999

The purpose of this study was to examine the influences of the consumer's knowledge the consumer's attitude the family characteristics and the variables on consumer socialization to the consumer's functions of homemakers. The samples were selected from 428 homemakers living in Kwangju, Frequncies Perentiles Means Standard Deviations Multiple regression Path analysis were used as statistical analysis The results were sumarized as follows: Resulting from multiple regression analysis the consumer's function had the positive linear relationships with variables such as family life cycles interaction with family consume knowledge and consumer attitude. The most influential variable was consumer attitude.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.839-847
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• 2011

In this study, we derive an estimator based on autocovariance for the regression coefficients vector in the multiple linear regression model. This method is suggested by Park (2009), and although this method does not seem to be intuitively attractive, this estimator is unbiased for the regression coefficients vector. When the vectors of exploratory variables satisfy some regularity conditions, under mild conditions which are satisfied when errors are from autoregressive and moving average models, this estimator has asymptotically the same distribution as the least squares estimator and also converges in probability to the regression coefficients vector. Finally we provide a simulation study that the forementioned theoretical results hold for small sample cases.

• Journal of applied mathematics & informatics
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• v.4 no.2
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• pp.501-512
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• 1997

In multiple linear regression model we have presupposed assumptions (independence normality variance homogeneity and so on) on error term. When case weights are given because of variance heterogeneity we can estimate efficiently regression parameter using weighted least squares estimator. Unfortunately this estimator is sen-sitive to outliers like ordinary least squares estimator. Thus in this paper we proposed some statistics for detection of outliers in weighted least squares regression.

• Korean Chemical Engineering Research
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• v.58 no.2
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• pp.248-256
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• 2020

In this study, predictive models based on machine learning for evaluating the extent of hazardous zone of explosive gases are developed. They are able to provide important guidelines for installing the explosion proof apparatus. 1,200 research data sets including 12 combustible gases and their extents of hazardous zone are generated to train predictive models. The extent of hazardous zone is set to an output variable and 12 variables affecting an output are set as input variables. Multiple linear regression, principal component regression, and artificial neural network are employed to train predictive models. Mean absolute percentage errors of multiple linear regression, principal component regression, and artificial neural network are 44.2%, 49.3%, and 5.7% and root mean square errors are 1.389m, 1.602m, and 0.203 m respectively. Therefore, it can be concluded that the artificial neural network shows the best performance. This model can be easily used to evaluate the extent of hazardous zone for explosive gases.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.755-768
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• 2017

As Big Data becomes the core of the fourth industrial revolution, big data-based processing and analysis capabilities are expected to influence the company's future competitiveness. Comparative studies of RHadoop and RHIPE that integrate R and Hadoop environment, have not been discussed by many researchers although RHadoop and RHIPE have been discussed separately. In this paper, we constructed big data platforms such as RHadoop and RHIPE applicable to large scale data and implemented the machine learning algorithms such as multiple regression and logistic regression based on MapReduce framework. We conducted a study on performance and scalability with those implementations for various sample sizes of actual data and simulated data. The experiments demonstrated that our RHadoop and RHIPE can scale well and efficiently process large data sets on commodity hardware. We showed RHIPE is faster than RHadoop in almost all the data generally.

• Journal of Korean Public Health Nursing
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• v.32 no.1
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• pp.30-43
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• 2018

Purpose: This study was to conducted to investigate the prevalence and related factors of metabolic syndrome (MS) among Korean adults. Methods: We used secondary data of the sixth Korean National Health and Nutrition Examination Survey (KNHANES) from 2013 to 2015 and selected 4,939 adults 20 to 64 years old. General characteristics and health-related characteristics were included as related factors for analysis. Chi-square tests were used to compare the prevalence of MS between one-person and multiple-person households, while a multiple logistic regression model was used to identify factors to MS among one-person and multiple-person households. Results: MS of one-person households (26.4%) were significantly higher (${\chi}^2=7.81$, p=.017) than multiple-households (19.5%). Risk factors for MS were identified as walking, flexibility exercises, reading nutrition labels, and sleep hours in one-person households; and flexibility exercises and dietary control among multiple-person households using multiple logistic regression. Conclusion: Specialized health policies and programs should be provided to reduce MS prevalence in one-person households in consideration of risk factors revealed in this study.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.10
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• pp.1633-1641
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• 2020

Objective: The objective of this study was to develop a model for estimating the carcass weight of Hanwoo cattle as a function of body measurements using three different modeling approaches: i) multiple regression analysis, ii) partial least square regression analysis, and iii) a neural network. Methods: Data from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal Science in South Korea. Among the 372 variables in the raw data, 20 variables related to carcass weight and body measurements were extracted to use in multiple regression, partial least square regression, and an artificial neural network to estimate the cold carcass weight of Hanwoo cattle by any of seven body measurements significantly related to carcass weight or by all 19 body measurement variables. For developing and training the model, 100 data points were used, whereas the 34 remaining data points were used to test the model estimation. Results: The R² values from testing the developed models by multiple regression, partial least square regression, and an artificial neural network with seven significant variables were 0.91, 0.91, and 0.92, respectively, whereas all the methods exhibited similar R² values of approximately 0.93 with all 19 body measurement variables. In addition, relative errors were within 4%, suggesting that the developed model was reliable in estimating Hanwoo cattle carcass weight. The neural network exhibited the highest accuracy. Conclusion: The developed model was applicable for estimating Hanwoo cattle carcass weight using body measurements. Because the procedure and required variables could differ according to the type of model, it was necessary to select the best model suitable for the system with which to calculate the model.