• 응용통계연구
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• 제23권5호
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• pp.909-921
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• 2010

패널 자료에서 무응답이 발생한 경우에는 횡시점회귀대체법(cross-wave regression imputation) 등과 같은 대체법을 이용하여 무응답 문제를 해결한다. 최근 표본 틀(sampling frame) 자료를 이용하여 무응답 가중치 보정을 하는 BLS 무응답 보정법은 패널 자료에도 적용 가능한 방법으로 알려져있다. 본 논문에서는 패널자료에서 BLS 무응답 보정법을 이용한 대체법을 연구하였으며 자료가 경향이 있는 비정상시계열(nonstationary process with drift)을 따른 다는 조건하에서 BLS 무응답 보정법과 횡시점회귀대체법의 하나인 이월대체법(carry-over imputation)과의 이론적 관계를 살펴보았다. 모의실험을 통하여 이론적인 결과를 확인하였으며, 2007년 매월노동통계 자료를 이용하여 두 방법의 우수성을 비교하였다.

• International Journal of Internet, Broadcasting and Communication
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• 제10권2호
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• pp.60-67
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• 2018

The purpose of this study was to examine the effects of internet-use Anatomy class, as one of the Flipped learning method, on critical thinking disposition. The class for this study was conducted from March 1 to April 10, 2018. The study involved a total of 180 people in the first year of a University located in C province. Data collection was carried out before and after the Flipped learning method application. Frequency analysis, Paired t-test, Pearson correlation, and Regression analysis were used for the analysis. According to the analysis, 28.3% of men and 71.1% of women and before applying the program analysis of correlation between Flipped learning perception and critical thinking disposition showed a significant correlation between confidence(sub-component of critical thinking) only (p<.005). Comparing the scores of critical thinking before and after the program, it was found that Truth seeking (p<.001), Open-mindness (p<.005), Confidence (p<.001), Systematicity (p<.005), Analyticity (p<.001), and Inquisitiveness (p<.001) scores had increased significantly except Maturity (p>.005). And the regression analysis of Flipped learning method applying influence on critical thinking disposition were significantly affected (p<.001). Based on the results of this study, it was possible to determine that Flipped learning method had a positive effect on critical thinking disposition.

• 대한인간공학회:학술대회논문집
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• 대한인간공학회 1993년도 추계학술대회논문집
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• pp.124-132
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• 1993

This study compared two prediction methods-regression and artificial neural network (ANN) on the visual search performance when monitoring a multi-parameter screen with different occurrence frequencies. Under the highlighting condition for the highest occurrence frequency parameter as a search cue, it was found from the requression analysis that variations of mean search time (MST) could be expained almost by three factors such as the number of parameters, the target occurrence frequency of a highlighted parameter, and the highlighted parameter size. In this study, prediction performance of ANN was evaluated as an alternative to regression method. Backpropagation method which was commonly used as a pattern associator was employed to learn a search behavior of subjects. For the case of increased number of parameters and incresed target occurrence frequency of a highlighted parameter, ANN predicted MST's moreaccurately than the regression method (p<0.000). Only the MST's predicted by ANN did not statistically differ from the true MST's. For the case of increased highlighted parameter size. both methods failed to predict MST's accurately, but the differences from the true MST were smaller when predicted by ANN than by regression model (p=0.0005). This study shows that ANN is a good predictor of a visual search performance and can substitute the regression method under certain circumstances.

• 조명전기설비학회논문지
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• 제17권6호
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• pp.54-59
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• 2003

본 논문에서는 퍼지 선형회귀분석법을 이용한 경쟁 전력시장에서의 전력의 시간별 현물가격을 예측하는 기법을 제시한다. 제안한 기법은 2002년 봄의 일주일에 대한 시간별 수요을 예측하여 본 기법의 타당성과 정확도를 검증하였다. 제안한 방법의 예측 오차는 주중의 경우 3.14%∼6.10%이며, 주말의 경우 7.04%∼8.22%로써 뉴럴 네트워크 기법을 이용한 방법과 비교하여 타당한 결과를 보였다.

• 품질경영학회지
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• 제25권2호
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• pp.140-153
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• 1997

As product quality is maily determined in the product design and process design step, systematic design should be performed through parameter design and tolerance design. Therefore, we introduced analysis of variance and regression analysis as a statistical method which determine optimal levels of affective design factors to product characteristics, then we compared that process and result. In analysis of variance, variation of quality characteristics arises from noise factors, so the optimal levels of design factors are selected to minimize the effect of noise factors. In regression analysis, variation of quality characteristics aries from variation of each own design factors. As a method to reduce variation of these quality characteristics, sensitivity analysis was performed about each design factors. Through this sensitivity analysis, we represented process to calculate the interaction term of the factors.

• 한국터널지하공간학회 논문집
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• 제15권2호
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• pp.113-122
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• 2013

최근 터널 갱구부와 저토피 구간에 대한 환경 친화적인 터널공법에 대한 관심이 증가하고 있다. 콘크리트 슬래브를 이용한 반개착식 터널공법을 적용한 연구 및 시공사례가 증가하고 있으나, 하중조합 및 아치콘크리트의 단면에 대한 별도의 적용 기준이 없다. 따라서 본 연구에서는 반개착식 공법에 대한 기준을 제시하기 위하여 지반조건과, 터널 상부 지층 두께, 되메움 두께, 상부지반 지표면의 경사 각도를 변화시키는 등의 다양한 조건들에 대하여 수치해석을 수행하였다. 수치해석 결과들을 회귀분석법으로 분석하고, 유도된 회귀 분석식을 분류하고 정리하여 합리적이고, 경제적이며, 안전한 반개착식 터널 굴착 공법의 기준을 제안하고자 하였다.

• Computers and Concrete
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• 제5권5호
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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.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2004년도 춘계학술대회
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• pp.91-101
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• 2004

The turbulent flow is of fundamental interest because the conservation equations for thermodynamics, mass and momentum are linked together. This turbulent flow consists of some coherent time- and space-organized vortical structures. Research has already shown that some dynamic systems and experimental models still cannot provide a good nonlinear analysis of turbulent time series. In the real turbulent flow, very complicated nonlinear behaviors, which are affected by many vague factors are present. In this paper, a kernel-based machine for fuzzy nonlinear regression analysis is proposed to predict the nonlinear time series of turbulent flows. In order to show the practicality and usefulness of this model, we present an example of predicting the near-wall turbulence time series as a verifiable model and compare with fuzzy piecewise regression. The results of practical applications show that the proposed method is appropriate and appears to be useful in nonlinear analysis and in fuzzy environments to predict the turbulence time series.

• 산업기술연구
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• 제34권
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• pp.53-59
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• 2014

The single regression method was used to analyze the correlationship between the compression index with mechanical properties for reclaimed marine clays in the southern coast of Korea. As results of performing regression analysis for 200 samples about reclaimed marine clays in the southern coast of Korea, linear regression lines between compression index and natural water content, void ratio in situ, and liquid limit respectively wer obtained. The changed properties of soil due to disturbance during dredging and reclaiming could be investigated by comparing with the existing empirical correlation equations for the original ground where dredging was performed. These regression equations might be rationally used in the preliminary evaluation of settlement of dredged and reclaimed marine clayey ground in the southern coast of Korea.

• 한국측량학회지
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• 제33권3호
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• pp.211-218
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• 2015

The analysis of remote sensing data depends on sensor specifications that provide accurate and consistent measurements. However, it is not easy to establish confidence and consistency in data that are analyzed by different sensors using various radiometric scales. For this reason, the cross-calibration method is used to calibrate remote sensing data with reference image data. In this study, we used an airborne hyperspectral image in order to calibrate a multispectral image. We presented an automatic cross-calibration method to calibrate a multispectral image using hyperspectral data and spectral mixture analysis. The spectral characteristics of the multispectral image were adjusted by linear regression analysis. Optimal endmember sets between two images were estimated by spectral mixture analysis for the linear regression analysis, and bands of hyperspectral image were aggregated based on the spectral response function of the two images. The results were evaluated by comparing the Root Mean Square Error (RMSE), the Spectral Angle Mapper (SAM), and average percentage differences. The results of this study showed that the proposed method corrected the spectral information in the multispectral data by using hyperspectral data, and its performance was similar to the manual cross-calibration. The proposed method demonstrated the possibility of automatic cross-calibration based on spectral mixture analysis.