• Korean journal of applied entomology
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• v.26 no.1 s.70
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• pp.31-36
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• 1987

A piecewise linear regression model able to describe disease progress curves with simplicity and flexibility was developed in this study. The model divides whole epidemic into several pieces of simple linear regression based on changes in pattern of disease progress in the epidemic and then incorporates the pieces of linear regression into a single mathematical function using indicator variables. When twelve epidemic data obtained from the field experiments were fitted to the piecewise linear regression model, logistic model and Gompertz model to compare statistical fit, goodness of fit was greatly improved with piecewise linear regression compared to other two models. Simplicity, flexibility, accuracy and ease in parameter estimation of the piece-wise linear regression model were described with examples of real epidemic data. The result in this study suggests that piecewise linear regression model is an useful technique for modeling plant disease epidemic.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.4
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• pp.23-29
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• 2004

In this study, four kinds of $Si_3N_4$-based ceramic cutting tools with different sintering time were fabricated to investigate the relation among mechanical properties, grain size and tool life. They were used to turn gray cast iron at a cutting speed of 330m/min and depth of cut of 0.5mm and 1mm in dry, continuos cutting conditions. Multiple linear regression model was used to determine the relations among the mechanical property, grain size and the density. It was found that the combination of hardness and fracture toughness showed a good relation with tool life. It was also shown that hardness was the most important single element for the tool life.

• Communications for Statistical Applications and Methods
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• v.12 no.2
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• pp.313-322
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• 2005

In this paper, we discuss suppression for logistic regression model. Suppression for linear regression model was defined as the relationship among sums of squared for regression as well as correlation coefficients of. variables. Since it is not common to obtain simple correlation coefficient for binary response variable of logistic model, we consider cumulative logistic models with multinomial and ordinal response variables rather than usual logistic model. As number of category of a response variable for the cumulative logistic model gets collapsed into binary, it is found that suppressions for these logistic models are changed. These suppression results for cumulative logistic models are discussed and compared with those of linear model.

• Journal of Preventive Medicine and Public Health
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• v.22 no.1 s.25
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• pp.25-28
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• 1989

Sixty-two medical records of patients with coal workers' pneumoconiosis who died in hospital as coal workers' pneumoconiosis were analysed for study of the relationship between forced expiratory volume in one second ($FEV_1$) and life expectancy in coal workers'pneumoconiosis. In the group who died of asphyxia from hemoptysis, life expectancy were well fitted with $FEV_1$(p<0.05). But others were not well fitted to simple linear regression equation. The prevalence of ECG sign of col pulmonale was more in the group of cardiorespiratory failure than asphyxia group. So, in the case of far advanced cor pulmonale, it was difficult to predict life expectancy by simple linear regression equation

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.246-248
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• 2018

본 논문에서는 배터리 수명의 지표인 SOH(state of health) 추정 시 배터리 노화에 따라 방전 용량의 급격한 변화가 발생하면 SOH도 변화하게 된다. 이로 인해 잘못된 SOH의 정보를 가지고 오게 되며 배터리의 안정성 및 신뢰성에 문제가 된다. 본 논문에서는 방전 용량과 내부 저항의 선형적 관계를 확인하고, 방전 용량과 내부저항을 고려한 단순선형회귀모형(simple linear regression model)을 모델링하였다. 방전 용량의 급격한 변화나 오프라인 기반 방전 용량을 측정함에 어려움이 있는 경우 단순선형회귀모형에 따라 방전 용량을 추정하여 SOH를 보정하는 기법을 제안하고 이에 대한 검증을 수행하였다.

• Restorative Dentistry and Endodontics
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• v.43 no.2
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• pp.21.1-21.5
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• 2018

• Computers and Concrete
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• v.5 no.5
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• pp.449-459
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• 2008

This paper focuses on the application of two dimensional orthogonal polynomials in the regression analysis for the relationship of product parameters viz. compressive strength, bulk density and water absorption of fly ash cement bricks with other process parameters such as percentages of fly ash, sand and cement. The method has been validated by linear and non-linear two parameter regression models. The use of two dimensional orthogonal system makes the analysis computationally efficient, simple and straight forward. Corresponding co-efficient of determination and F-test are also reported to show the efficacy and reliability of the relationships. By applying the evolved relationships, the product parameters of fly ash cement bricks may be approximated for the use in construction sectors.

• Journal of Industrial Technology
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• v.25 no.A
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• pp.57-65
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• 2005

In this paper, for the highly plastic marine soft clay distributed in west and southern coast of Korean peninsula of Kwangyang and Busan New Port areas, correlation between compression index and other indices representing geotechnical engineering properties such as liquid limit, void ratio and natural water content were analyzed. Appropriate empirical equations of being able to estimate the compressibility of clays in the specific areas were proposed and compared with other existing empirical ones. For analyses of the data and test results, data for marine clays were used from areas of the South Container Port of the Busan New Port, East Breakwater, Passenger Quay, Jungma Reclamation and Reclamation Containment in the 3rd stage in Kwangyang. In order to find the best regression model by using the commercially available software, MS EXCEL 2000, results obtained from the simple linear regression analysis, using the values of liquid limit, initial void ratio and natural water content as independent variables, were compared with the existing empirical equations. Multiple linear regression was also performed to find the best fit regression curves for compression index and other soil properties by combining those independent variables. On the other hands, another software of SPSS for non-linear regression was used to analyze the correlations between compression index and other soil properties.

• Journal of the Korean Society for Railway
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• v.19 no.4
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• pp.547-554
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• 2016

This study considered how to estimate the deterioration rate of the track quality index, which represents track geometric irregularity. Most existing studies have used a simple linear regression and regarded the slope of the regression equation as the progress rate. In this paper, we present a Bayesian approach to estimate the track irregularity progress. This Bayesian approach has many advantages, among which the biggest is that it can formally include the prior distribution of parameters which can be derived from historic data or from expert experiences; then, the rate can be expressed as a probability distribution. We investigated the possibility of applying the Bayesian method to the estimation of the deterioration rate by comparing our bayesian approach to the conventional linear regression approach.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.187-191
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• 2000

In the analysis of repeated measurements, multivariate regression models that account for the correlations among the observations from the same subject are widely used. Like the usual univariate regression models, these multivariate regression models also need some model diagnostic procedures. In this paper, we propose a simple graphical method to detect outliers and to investigate the goodness of model fit in repeated measures data. The graphical method is based on the quantile-quantile(Q-Q) plots of the $X^2$ distribution and the standard normal distribution. We also propose diagnostic measures to detect influential observations. The proposed method is illustrated using two examples.