• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.921-934
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• 2004

We consider the problem of identifying and testing outliers in linear regression. First, we consider the scale-ratio tests for testing the null hypothesis of no outliers. A test based on the ratio of two residual scale estimates is proposed. We show the asymptotic distribution of test statistics and investigate the properties of the test. Next we consider the problem of identifying the outliers. A forward procedure based on the suggested test is proposed and shown to perform fairly well. The forward procedure is unaffected by masking and swamping effects because the test statistics used a robust scale estimate.

• Korean Journal of Veterinary Research
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• v.53 no.2
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• pp.73-76
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• 2013

The aim of this study was to produce a simple and inexpensive technique for estimating the obturator foramen area (OFA) from young calves based on the hypothesis that OFA can be extrapolated from simple linear measurements. Three linear measurements - dorsoventral height, craneocaudal width and total perimeter of obturator foramen - were obtained from 55 bovine hemicoxae. Different algorithms for determining OFA were then produced with a regression analysis (curve fitting) and statistical analysis software. The most simple equation was OFA ($mm^2$) = [3,150.538 + ($36.111^*CW$)] - [147,856.033/DH] (where CW = craneocaudal width and DH = dorsoventral height, both in mm), representing a good nonlinear model with a standard deviation of error for the estimate of 232.44 and a coefficient of multiple determination of 0.846. This formula may be helpful as a repeatable and easily performed estimation of the obturator foramen area in young bovines. The area of the obturator foramen magnum can thus be estimated using this regression formula.

• Earthquakes and Structures
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• v.25 no.6
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• pp.429-442
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• 2023

To study the seismic vulnerability of the composite material structure of adobe and timber, we collected and statistically analysed empirical observation samples of 542,214,937 m² and 467,177 buildings that were significantly impacted during the 179 earthquakes that occurred in mainland China from 1976 to 2010. In multi-intensity regions, combined with numerical analysis and a probability model, a non-linear continuous regression model of the vulnerability, considering the empirical seismic damage area (number of buildings) and the ratio of seismic damage, was established. Moreover, a probability matrix model of the empirical seismic damage mean value was provided. Considering the coupling effect of the annual and seismic fortification factors, an empirical seismic vulnerability curve model was constructed in the multiple-intensity regions. A probability matrix model of the mean vulnerability index (MVI) was proposed, and was validated through the above-mentioned reconnaissance sample data. A matrix model of the MVI of the regions (19 provinces in mainland China) based on the parameter (MVI) was established.

• Journal of Environmental Health Sciences
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• v.48 no.4
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• pp.236-243
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• 2022

Background: In the case of multiple exposures to different types of heavy metals, such as the conditions faced by residents living near a smelter, it would be preferable to group hazardous substances with similar characteristics rather than individually related substances and evaluate the effects of each group on the human body. Objectives: The purpose of this study is to evaluate the utility of factor analysis in the assessment of health effects caused by exposure to two or more hazardous substances with similar characteristics, such as in the case of residents living near a smelter. Methods: Heavy metal concentration data for 572 people living in the vicinity of the Janghang smelter area were grouped based on several subfactors according to their characteristics using factor analysis. Using these factor scores as an independent variable, multiple regression analysis was performed on health effect markers. Results: Through factor analysis, three subfactors were extracted. Factor 1 contained copper and zinc in serum and revealed a common characteristic of the enzyme co-factor in the human body. Factor 2 involved urinary cadmium and arsenic, which are harmful metals related to kidney damage. Factor 3 encompassed blood mercury and lead, which are classified as related to cardiovascular disease. As a result of multiple linear regression analysis, it was found that using the factor index derived through factor analysis as an independent variable is more advantageous in assessing the relevance to health effects than when analyzing the two heavy metals by including them in a single regression model. Conclusions: The results of this study suggest that regression analysis linked with factor analysis is a good alternative in that it can simultaneously identify the effects of heavy metals with similar properties while overcoming multicollinearity that may occur in environmental epidemiologic studies on exposure to various types of heavy metals.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5765-5781
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• 2018

Extreme learning machine (ELM) is emerging as a powerful machine learning method in a variety of application scenarios due to its promising advantages of high accuracy, fast learning speed and easy of implementation. However, how to select the optimal hidden layer of ELM is still an open question in the ELM community. Basically, the number of hidden layer nodes is a sensitive hyperparameter that significantly affects the performance of ELM. To address this challenging problem, we propose to adopt multiple kernel learning (MKL) to design a multi-hidden-layer-kernel ELM (MHLK-ELM). Specifically, we first integrate kernel functions with random feature mapping of ELM to design a hidden-layer-kernel ELM (HLK-ELM), which serves as the base of MHLK-ELM. Then, we utilize the MKL method to propose two versions of MHLK-ELMs, called sparse and non-sparse MHLK-ELMs. Both two types of MHLK-ELMs can effectively find out the optimal linear combination of multiple HLK-ELMs for different classification and regression problems. Experimental results on seven data sets, among which three data sets are relevant to classification and four ones are relevant to regression, demonstrate that the proposed MHLK-ELM achieves superior performance compared with conventional ELM and basic HLK-ELM.

• Journal of Families and Better Life
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• v.19 no.1
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• pp.143-158
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• 2001

The purpose of this study was to examine the influences of the material values, attitudes toward money, and the background variables to the money use behavior of female collegians. The samples were selected from 541 female collegians. Cronbach$\alpha$, One-way ANOVA, Duncan test, Multiple regression, Path analysis were used as statistical analysis. The results were summarized as follows : Resulting from multiple regression analysis, the money use behavior of female collegians had the positive linear relationships with the variables such as mothers level of education, material values, and 3 money attitude - the means of security, the symbol of anxiety, the means of pleasure - in five money attitude dimensions. The most influential variable was money attitude of the means of security.

• Journal of Applied Biological Chemistry
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• v.44 no.4
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• pp.194-198
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• 2001

Contaminated sites are often abandoned for an extended period of time, thus organopollutants becoming sequestered. The information on the initial concentrations of the contaminants would be helpful for the economic bioremediation of the chemicals. The present study estimated the initial concentrations of atrazine and phenanthrene through multiple regression analyses using soil properties and the amount of chemicals available in situ. Percentage mineralized or extracted was best correlated with organic C or logarithm of organic C, and the $R^2$ values were 0.548 and 0.894 for atrazine and phenanthrene, respectively. Estimation of the initial concentration of the chemicals was then calculated from both the percentage mineralized or extracted and the amount of chemicals extracted or mineralized. Results showed that the estimation of the initial concentration of the chemical at the time of contamination is feasible.

• Journal of Korean Home Economics Education Association
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• v.13 no.2
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• pp.37-46
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• 2001

The purpose of this study was to examine the influences of 4-value orientations. the 5 money spending styles. and the background variables to the desired money management behavior of high school girls. The samples were selected from 498 high school girls. Cronbach'a and Multiple regression were used as statistical analysis. The results were summarized as follows : Resulting from multiple regression analysis, the desired money management behavior of high school girls had the positive linear relationships with the variables such as financial satisfaction. goal-oriented value. and 4 money spending styles - security. conspicuous. non-monetary. and unconcerned - in five money spending styles. The most influential variable was money spending styles.

• Journal of Korean Home Economics Education Association
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• v.13 no.2
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• pp.27-27
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• 2001

The purpose of this study was to examine the influences of 4-value orientations the 5 money spending styles. and the background variables to the desired money management behavior of high school girls The samples were selected from 498 high school girls. Cronbach'α and Multiple regression were used as statistical analysis.The results were summarized as follows : Resulting from multiple regression analysis, the desired money management behavior of high school girls had the positive linear relationships with the variables such as financial satisfaction. goal-oriented value, and 4 money spending styles - security, conspicuous, non-monetary, and unconcerned - in five money spending ,styles. The most influential variable was money spending styles.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.851-856
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• 1997

This study is on the estimation of the tool temperature for different tool nose radius and cutting conditions in turning. The experiment has been performed in different cutting conditions such as cutting speed, feed rate, and depth of cut for the tool nose radius, 0.4R, 0.8R using SMC workpiece materials. Tool temperature is measured using thermo-couple which is embedded in the insert tip. Using a multiple linear regression method, the tool temperature can be determined as an exponential equation with cutting variables and tool nose diameters for different tool materials. The equations determined in this study show a good correlation for the cutting conditions and can be used for the tool temperature estimation. The result indicates that the tool temperature decreases for ~ncreasing the tool nose radius in general. Also, nose radius hardly influences on the tool temperature compared with cutting speed, feed rate and depth of cut.