• Journal of the Korean Data and Information Science Society
• /
• v.21 no.3
• /
• pp.513-521
• /
• 2010

Most of universities have carried out course evaluation to apply the performance appraisal for professor. But, course evaluation depends on characteristics of each class such as class size, type of lecture, evaluator's grade and so on. As the results, such characteristics of each class lead to serious bias which makes lecturers distrust the course evaluation results. Hence, we propose a equating method for the course evaluation by regression analysis which use stepwise variable selection. And we compare proposed method with the other method by Cho et al. (2009) with respect to efficiencies. Also we give the example to which the method is applied.

• The Transactions of the Korean Institute of Electrical Engineers C
• /
• v.52 no.3
• /
• pp.137-143
• /
• 2003

The degree of contamination on outdoor insulator is ons of the most importance factor to determine the pollution level of outdoor insulation, and the sea salt is known as the most dangerous pollutant. As shown through the preceding study, the generation of salt pollutant and the pollution degree of outdoor insulator have a close relation with meteorological conditions, such as wind velocity, wind direction, precipitation and so fourth. So, in this paper, we made an investigation on the prediction method, a statistical estimation technique for equivalent salt deposit density of outdoor insulator with multiple linear regression analysis. From the results of the analysis, we proved the superiority of the prediction method in which the variables had a very close(about 0.9) correlation coefficient. And the results could be applied to establish the Pollution Prediction System for power utilities, and the system could provide an invaluable information for the design and maintenance of outdoor insulation system.

• The Transactions of the Korea Information Processing Society
• /
• v.3 no.6
• /
• pp.1468-1472
• /
• 1996

In this study, the performance of the CART(Classification and Regression Tree) is compared with that of discriminant analysis method. In most experiments using bank data, discriminant analysis shows better performance in terms of the total cost. In contrast, most experiments using insurance data show that the CART is better than discriminant analysis in terms of the total cost. The contradictory result are analysed by using the characteristics of the data sets. The performances of both the Classification and Regression Tree and discriminant analysis depend on the parameters:failure prior probability, data used, type I error, type II error cost, and validation method.

• The Journal of Engineering Geology
• /
• v.24 no.1
• /
• pp.101-110
• /
• 2014

This study presents the results of a regression analysis of the point-load strength ($I_{s(50)}$) and the uniaxial compressive strength (UCS) of granites in Korea. The regression was carried out for three cases using the least-squares method, reclassifying the granite samples based on their physical properties. The first regression analysis through the origin according to the weathering grade did not give a result with a sufficient degree of confidence, due to the small number of samples. However, the general trend of the correlation between UCS and $I_{s(50)}$according to weathering grade shows that the slope of the linear regression for weathered granite is steeper than that for fresh granite. The second analysis was a simple linear regression for all the granite samples using the least-squares method as well as a linear regression using the bootstrap resampling method in order to increase the confidence level and the accuracy of the regression results. The third regression considered the average strength of granite groups reclassified according to physical properties. These linear regression analyses yielded linear regression equations with slopes of 14 and small standard deviations being similar to values reported in previous studies on Korean granites, but whose intercept values range from 16 to 43 and have a larger standard deviation than those of the present study. In conclusion, it would be advisable to estimate UCS from $I_{s(50)}$, considering the error range derived from the deviation of the regression equations.

• Proceedings of the Korean Geotechical Society Conference
• /
• 1993.03a
• /
• pp.27-32
• /
• 1993

In tunnelling by NATM convergence data are most Importantly to ascertain the safety of tunnel. Therefore, a reliable method is required that can predict ultimate displacements by using earler displacement data. Displacement forecasting method is classified into statistical method and functional regression method. Convergence data measured in Seoul subway 5~45 site during '92.5 ~ '92.12 were analyzed by above said two methods. The analysis results of convergence data show that the functional regression method is more relieable in completely weathered rock, but the statistical method in slightly wearhred rock.

• Bulletin of the Korean Chemical Society
• /
• v.22 no.1
• /
• pp.37-42
• /
• 2001

Ridge regression is compared with multiple linear regression (MLR) for determination of Research Octane Number (RON) when the baseline and signal-to-noise ratio are varied. MLR analysis of near-infrared (NIR) spectroscopic data usually encounters a collinearity problem, which adversely affects long-term prediction performance. The collinearity problem can be eliminated or greatly improved by using ridge regression, which is a biased estimation method. To evaluate the robustness of each calibration, the calibration models developed by both calibration methods were used to predict RONs of gasoline spectra in which the baseline and signal-to-noise ratio were varied. The prediction results of a ridge calibration model showed more stable prediction performance as compared to that of MLR, especially when the spectral baselines were varied. . In conclusion, ridge regression is shown to be a viable method for calibration of RON with the NIR data when only a few wavelengths are available such as hand-carry device using a few diodes.

• The Korean Journal of Applied Statistics
• /
• v.26 no.4
• /
• pp.687-696
• /
• 2013

The detection and the examination of outliers are important parts of data analysis because some outliers in the data may have a detrimental effect on statistical analysis. Outlier detection methods have been discussed by many authors. In this article, we propose to apply Hadi and Simonoff's (1993) method to penalized spline a regression model to detect multiple outliers. Simulated data sets and real data sets are used to illustrate and compare the proposed procedure to a penalized spline regression and a robust penalized spline regression.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.14 no.2
• /
• pp.105-114
• /
• 1989

The actual working time of jobs, in general, is different to the standard time of jobs. In this paper, in order to analyze the actual working time of each job in production, we use the total production amount and the encessary total working time. The method which analyzes the actual working time is as follows. In this paper, we propose the interval regression analysis for obtaining an interval linear regression model with constraint conditions with respect to interval parameters. The merits of this method are the following.1) it is easy to obtain an interval linear model by solving a LP problem to which the formulation of proposed regression analysis is reduced, 2) it is easy to add constraint conditions about interval parameters, which are a sort of expert knowledge. As an application, within a case which has given certain data, the actual working time of jobs and the number of workers in a future plan are estimated through the real data obtianed from the operation of processing line in a heavy industry company. It results from the proposed method that the actual working time and the number of workers can be estimated as intervals by the interval regression model.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.8
• /
• pp.3086-3101
• /
• 2021

To supply precise marketing and differentiated service for the electric power service department, it is very important to predict the customers with high sensitivity of electric power failure. To solve this problem, we propose a novel grouped 𝑙_1/2 sparsity constrained logistic regression method for sensitivity assessment of electric power failure. Different from the 𝑙₁ norm and k-support norm, the proposed grouped 𝑙_1/2 sparsity constrained logistic regression method simultaneously imposes the inter-class information and tighter approximation to the nonconvex 𝑙₀ sparsity to exploit multiple correlated attributions for prediction. Firstly, the attributes or factors for predicting the customer sensitivity of power failure are selected from customer sheets, such as customer information, electric consuming information, electrical bill, 95598 work sheet, power failure events, etc. Secondly, all these samples with attributes are clustered into several categories, and samples in the same category are assumed to be sharing similar properties. Then, 𝑙_1/2 norm constrained logistic regression model is built to predict the customer's sensitivity of power failure. Alternating direction of multipliers (ADMM) algorithm is finally employed to solve the problem by splitting it into several sub-problems effectively. Experimental results on power electrical dataset with about one million customer data from a province validate that the proposed method has a good prediction accuracy.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2012.05a
• /
• pp.709-710
• /
• 2012