• 한국임상수의학회지
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• 제27권4호
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• pp.427-434
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• 2010

Correlation is a technique used to measure the strength or the degree of closeness of the linear association between two quantitative variables. Common misuses of this technique are highlighted. Linear regression is a technique used to identify a relationship between two continuous variables in mathematical equations, which could be used for comparison or estimation purposes. Specifically, regression analysis can provide answers for questions such as how much does one variable change for a given change in the other, how accurately can the value of one variable be predicted from the knowledge of the other. Regression does not give any indication of how good the association is while correlation provides a measure of how well a least-squares regression line fits the given set of data. The better the correlation, the closer the data points are to the regression line. In this tutorial article, the process of obtaining a linear regression relationship for a given set of bivariate data was described. The least square method to obtain the line which minimizes the total error between the data points and the regression line was employed and illustrated. The coefficient of determination, the ratio of the explained variation of the values of the independent variable to total variation, was described. Finally, the process of calculating confidence and prediction interval was reviewed and demonstrated.

• Communications for Statistical Applications and Methods
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• 제26권1호
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• pp.47-55
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• 2019

It is important to identify informative variables in high dimensional data analysis; however, it becomes a challenging task when covariates are contaminated by measurement error due to the bias induced by measurement error. In this article, we present a two-step approach for variable selection in the presence of measurement error. In the first step, we directly select important variables from the contaminated covariates as if there is no measurement error. We then apply, in the following step, orthogonal regression to obtain the unbiased estimates of regression coefficients identified in the previous step. In addition, we propose a modification of the two-step approach to further enhance the variable selection performance. Various simulation studies demonstrate the promising performance of the proposed method.

• 품질경영학회지
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• 제21권1호
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• pp.161-170
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• 1993

This study attempts to present the linear regression analysis that involves more than one regressor variable, because regression analysis is the most widely used statistical technique for describing, predicting and estimating the relationships between given data. The model of multiple linear regression may be solved directly by the two linear programming methods, i.e., to minimize the sum of the absolute deviation (MSD) and to minimize the maximum deviation(MMD). In addition, some results was compared to each techniques for accuracy and tested to the validity of statistical meaning.

• 한국컴퓨터정보학회논문지
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• 제26권8호
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• pp.157-164
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• 2021

회귀분석은 반응변수와 예측변수들 간의 관련성을 설명하기 위해 사용되는 잘 알려진 통계 테크닉이다. 특히 연구자들은 두 개의 독립 모집단에서의 모형들의 회귀계수들(절편과 기울기)을 비교하는데 관심이 있다. Gregory Chow에 의해 제안된 Chow 검정은 회귀모형들을 비교하고 선형회귀모형 안에 구조적 브레이크가 존재하는지를 검정하기 위해 보통 사용되는 방법들 중의 하나이다. 본 연구에서는 두 독립 선형회귀모형들의 등가성을 검정하기 위해 퍼뮤테이션 방법을 제안하고 Chow 검정과 비교한다. 그리고 퍼뮤테이션 검정과 Chow 검정의 검정력을 조사하기 위해 시물레이션 연구를 진행하였다.

• 한국경영과학회지
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• 제20권3호
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• pp.17-29
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• 1995

Recently, neural network models have been employed as an alternative to regression analysis for point estimation or function fitting in various field. Thus far, however, no theoretical or empirical guides seem to exist for selecting the tool which the most suitable one for a specific function-fitting problem. In this paper, we evaluate performance of three major function-fitting techniques, regression analysis and two neural network models, back-propagation and linear-Hebbian-learning neural networks. The functions to be fitted are simple linear ones of a single independent variable. The factors considered are size of noise both in dependent and independent variables, portion of outliers, and size of the data. Based on comutational results performed in this study, some guidelines are suggested to choose the best technique that can be used for a specific problem concerned.

• Communications for Statistical Applications and Methods
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• 제22권1호
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• pp.41-54
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• 2015

In this article, we propose nonconcave penalties on a reduced-rank regression model to select variables and estimate coefficients simultaneously. We apply HARD (hard thresholding) and SCAD (smoothly clipped absolute deviation) symmetric penalty functions with singularities at the origin, and bounded by a constant to reduce bias. In our simulation study and real data analysis, the new method is compared with an existing variable selection method using $L_1$ penalty that exhibits competitive performance in prediction and variable selection. Instead of using only one type of penalty function, we use two or three penalty functions simultaneously and take advantages of various types of penalty functions together to select relevant predictors and estimation to improve the overall performance of model fitting.

• KIEAE Journal
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• 제15권5호
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• pp.13-20
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• 2015

Purpose: In order to upgrade the energy performance of existing building, energy audit stage should be implemented first because it is useful method to find where the problems occur and know how much time and cost consumption for retrofit. In overseas researches, three levels of audit is proposed whereas there are no standards for audit in Korea. Besides, most studies use dynamic simulation in detail like audit level 3 even though the level 2 can save time and cost than level 3. Thus, this paper focused on audit level 2 and proposed the audit method with the simple linear regression analysis model. Method: Two parameters were considered for the simple regression analysis, which were the monthly electric use and the mean outdoor temperature data. The former is a dependent variable and the latter is a independent variable, and the building's energy performance profile was estimated from the regression analysis method. In this analysis, we found the abnormal point in cooling season and the more detailed analysis were conducted about the three heat source equipments. Result: Comparing with real and predicted models, the total consumption of predicted model was higher than real value as 23,608 kWh but it was the results that was reflected the compulsory control in 2013. Consequently, it was analyzed that the revised model could save the cooling energy as well as reduce peak electric use than before.

• 한국항공우주학회지
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• 제32권4호
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• pp.80-87
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• 2004

KSR-III 비행용 액체추진제 로켓엔진의 각 성능 변수 간 상관관계를 파악하기 위하여, 엔 진 지상연소시험의 결과에 대한 분석이 수행되었다. 내열재 연소실의 삭마에 따른 변화를 고려하였으며, 산화제/연료비에 의한 변화를 무시한 선형 회귀분석과 이를 포함한 이변수 이차 회귀분석이 수행되었다. 선형 회귀분석은 간단하면서도 분석영역 내에서 1% 이내의 오차율을 가지는 매우 실용적인 방법임을 보여주었다. 또한 이변수 이차 회귀분석 결과는 분석영역 내에서 매우 높은 정확도의 예측이 가능하였으며, KSR-III 엔진의 추력 (혹은 비추력) 및 연소실 압력 (혹은 특성속도)에 대한 최적 산화제/연료비가 각각 2.22 와 2.17 인 것으로 분석되었다.

• 응용통계연구
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• 제18권2호
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• pp.435-443
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• 2005

로지스틱 회귀모형에서 결정계수는 선형 회귀모형보다 다양하게 정의되며 그 값들도 매우 작아 로지스틱 회귀모형 평가기준으로 사용되는 통계량이 라고 할 수 없다. Liao와 McGee(2003)는 부적절한 설명변수의 추가 또는 표본크기의 변화에 민감하지 않은 두 종류의 수정 결정계수를 제안하였다. 본 연구에서는 실제자료에 적용한 로지스틱 회귀모형에서 수정 결정계수를 포함한 네 종류의 결정계수들을 변수선택의 기준으로 사용하여 기존의 변수선택 방법인 전진선택, 후진제거, 단계적 선택방법, AIC 통계량 등을 사용한 방법들과 비교하여 그 적절함과 효율성을 토론한다.

• 대한수학회논문집
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• 제31권1호
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• pp.185-198
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• 2016

Regression analysis is an analyzing method of regression model to explain the statistical relationship between explanatory variable and response variables. This paper propose a fuzzy regression analysis applying Theils method which is not sensitive to outliers. This method use medians of rate of increment based on randomly chosen pairs of each components of ${\alpha}$-level sets of fuzzy data in order to estimate the coefficients of fuzzy regression model. An example and two simulation results are given to show fuzzy Theils estimator is more robust than the fuzzy least squares estimator.