• 한국물환경학회지
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• 제29권5호
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• pp.625-629
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• 2013

The objective of this study is to develop and evaluate simple and multiple regression models for Total Organic Carbon (TOC) concentration estimation in stream flow. For development (using water quality data in 2012) and evaluation (using water quality data in 2011) of regression models, we used water quality data from downstream of Yeongsan river basin during 2011 and 2012, and correlation analysis between TOC and water quality parameters was conducted. The concentrations of TOC were positively correlated with Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), TN (Total Nitrogen), Water Temperature (WT) and Electric Conductivity (EC). From these results, simple and multiple regression models for TOC estimation were developed as follows : $TOC=0.5809{\times}BOD+3.1557$, $TOC=0.4365{\times}COD+1.3731$. As a result of the application evaluation of the developed regression models, the multiple regression model was found to estimate TOC better than simple regression models.

• Restorative Dentistry and Endodontics
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• 제44권1호
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• pp.11.1-11.8
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• 2019

In the previous sections, simple linear regression (SLR) 1 and 2, we developed a SLR model and evaluated its predictability. To obtain the best fitted line the intercept and slope were calculated by using the least square method. Predictability of the model was assessed by the proportion of the explained variability among the total variation of the response variable. In this session, we will discuss four basic assumptions of regression models for justification of the estimated regression model and residual analysis to check them.

• Communications for Statistical Applications and Methods
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• 제9권2호
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• pp.459-471
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• 2002

In applications using a linear regression model with nested error structure, one might be interested in making inferences concerning variance components. This article proposes approximate confidence intervals on the variance component of the primary level in a simple linear regression model with an unbalanced nested error structure. The intervals are compared using computer simulation and recommendations are provided for selecting an appropriate interval.

• Communications for Statistical Applications and Methods
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• 제19권6호
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• pp.793-798
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• 2012

In this paper, we propose regression methods based on the likelihood function. We assume Arnold-Beaver Skew Normal(ABSN) errors in a simple linear regression model. It was shown that the novel method performs better with an asymmetric data set compared to the usual regression model with the Gaussian errors. The utility of a novel method is demonstrated through simulation and real data sets.

• 대기
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• 제17권2호
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• pp.135-145
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• 2007

We developed a simple regression model for predicting the intesity change (central pressure) of major tropical cyclones (TCs) for 24 hours after landfall using 51 TC cases which landed over the Korean Peninsula for 1951-2004. Clusters 1 and 4 with a relatively strong intensity of TC after landfall classified by Choi and Kim (2007) are used to develop a statistical model for the prediction of TC intensity change. Predicting parameters (falling constants) in the regression models $(P_t=P_0+alnt)$ are 6.46 and 10.11 for clusters 1 and 4, respectively. It might be mentioned that there is some feasibility in employing a simple regression model developed in this study for TC intensity change after landfall for operational purpose of TC forecasting compared with RSMC-Tokyo best-track in both TC cases of Clusters 1 and 4 and Ewiniar (0603) case, but the room for improvement of model still remains for further study.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.319-325
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• 2001

In this note, we propose a simple fuzzy linear regression model for fuzzy input-output data based on Tanaka's approach. Then an LP-based method to derived the satisfying solution of the decision making is developed.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.257-266
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• 2003

Distribution-free estimation methods are proposed for slope, $\beta$ in the simple linear regression model. In this paper, we suggest the point estimators using the sequential slope based on sign test and Wilcoxon signed rank test. Also confidence intervals are presented for each estimation methods. Monte Carlo simulation study is carried out to compare the efficiency of these methods with least square method and Theil´s method. Some properties for the proposed methods are discussed.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.361-370
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• 2003

Those who are interested in making inferences concerning linear combination of valiance components in a simple linear regression model with unbalanced nested error structure can use the confidence intervals proposed in this paper. Two approximate confidence intervals for the sum of two variance components in the model are proposed. Simulation study is peformed to compare the methods. The methods are applied to a numerical example and recommendations are given for choosing a proper interval.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2003년도 춘계 학술발표회 논문집
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• pp.75-78
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• 2003

Those who are interested in making inferences concerning linear combination of variance components in a simple linear regression model with unbalanced nested error structure can use the confidence intervals proposed in this paper. Two approximate confidence intervals for the sum of two variance components in the model are proposed. Simulation study is peformed to compare the methods.

• Journal of the Korean Data and Information Science Society
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• 제20권2호
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• pp.251-260
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• 2009

이 논문에서는 단순 선형회귀 모형에서 회귀 계수의 최적 추정량을 구할 수 있는 자기공분산에 근거한 추정 방법을 제시하였다. 이 방법이 직관적으로 매혹적이지는 않지만 이 최적 추정량이 해당 회귀 계수의 불편추정량이 된다. 설명변수가 0과 1사이의 균등간격의 값을 가지면, 오차가 자기회귀 이동평균 모형을 따르면 성립하는 조건 하에서 이 최적 추정량이 최소제곱 추정량과 점근적으로 통일한 분포를 가진다는 것을 보였다. 추가적으로 똑같은 조건 하에서 이 최적 추정량이 해당 회귀 계수에 확률상 수렴한다는 것을 자체적으로 입증하였다.