• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2002년도 춘계공동학술대회
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• pp.949-952
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• 2002

A regression model is used in predicting the response variable given predictor variables However, in case of large number of predictor variables, a regression model has some problems such as multicollinearity, interpretation of the functional relationship between the response and predictors and prediction accuracy. A clustering method and stepwise regression could be used to reduce the amount of data by grouping predictors having similar properties and by selecting the subset of predictors. respectively. This paper proposes a prediction method combining clustering method and stepwise regression. The proposed method fits a global model and local models and predicts responses given new observations by using both models. The paper also compares the performance of proposed method with stepwise regression via a real data of ample obtained in a steel process.

• Archives of Pharmacal Research
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• 제11권2호
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• pp.99-113
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• 1988

Quantitation of ethenzamide, isopropylantipyrine and caffeine takes about 41 hrs by conventional GC method. Quantitation of allylisoprorylacetylurea takes about 40 hrs by conventional UV method. But quantitation of them takes about 6 hrs by DRIFT developing method. Each standard and sample sieved, powdered and acquired DRIFT spectrum. Out of them peak of each component was selected and ratio of each peak to standard peak was acquired, and then linear stepwise multiple regression was performed with these data and concentration. Reflectance value, Kubelka-Munk equation and Inverse-Kubelka-Munk equation were modified by us. Inverse-Kubelka-Munk equation completed the deficit of Kubelka-Munk equation. Correlation coefficients acquired by conventioanl GC and UV against DRIFT were more than 0.95.

• Journal of the Korean Statistical Society
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• 제7권2호
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• pp.105-108
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• 1978

Freund, Vail, and Ross, Goldberger and Jochems and Goldberger have given some results for the stepwise estimation of the parameters of a univariate regression model, D.G. Kabe gave similar results for a multivariate linear regression model. We give here alternative derivation of some results derived by D.G. Kabe.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권4호
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• pp.277-281
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• 2006

Cyber counseling, one of the most compatible type of consultation for the information society, enables people to reveal their mental agonies and private problems anonymously, since it does not require face-to-face interview between a counsellor and a client. However, there are few cyber counseling centers which provide high quality and trustworthy service, although the number of cyber counseling center has highly increased. Therefore, this paper is intended to enable an appropriate consultation for each client by analyzing client propensity using Bayesian variable selection. Bayesian variable selection is superior to stepwise regression analysis method in finding out a regression model. Stepwise regression analysis method, which has been generally used to analyze individual propensity in linear regression model, is not efficient since it is hard to select a proper model for its own defects. In this paper, based on the case database of current cyber counseling centers in the web, we will analyze clients' propensities using Bayesian variable selection to enable individually target counseling and to activate cyber counseling programs.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2013년도 춘계학술대회 논문집
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• pp.1513-1514
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• 2013

• 한국미생물·생명공학회지
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• 제13권3호
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• pp.285-287
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• 1985

To investigate effective constituents of the many taste components in ordinary Korean soy sauce, we analyzed free amino acids, organic acids, free sugars and saline as taste components in ordinary Korean soy sauce, and determined sensory score of the ordinary Korean soy sauce taste with 45 persons of the trained pannels. The relationships between original data transformed with variables and sensory score of the ordinary Korean soy sauce were analyzed by stepwise multiple regression analysis. Eighty five percents of the ordinary Korean soy sauce taste is affected by twenty one kinds (Isoleucine, Leucine, Valine, NaCl, Lactic acid, Alanine, Phenylalanine, Tartaric acid, Sugar(\ulcorner), Proline, Malic acid, Glycine, Tryptophan, Arginine, Glutaric acid, Maltose, Histidine, Glucose, Fructose and Serine) of the taste components by stepwise multiple regression analysis of original data. Eighty one percents of the ordinary Korean soy sance taste is affected by sixteen kinds (Lactic acid, NaCl, Fumaric.Succinic acid, Tyrosine, Tartaric acid, Glycine, Malonic acid, Malic acid, Tryptophan, Glutaric acid, Methionine, Histidine, Cysteine, Maltose, Fructose and (Glutamic acid) of the taste components by stepwise multiple frgression analysis of original data transformed with square root. Eighty five percents of the ordinary Korean soy sauce taste is affected by nineteen kinds (Fumaric.Succinic acid, Lactic acid, Phenylalanine, NaCl, Tyrosine, Sugar(\ulcorner), Tartaric acid, Leucine, Glutaric acid, Methionine, Glycine, Tryptophan, Histidine, Proline, Cysteine, Glutamic acid, Maltose, Threonine and Oxalic acid) of the taste components by stepwise multiple regression analysis of original data transformed with logarithm.

• 대한산업공학회지
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• 제42권5호
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• pp.314-326
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• 2016

The purpose of variable selection techniques is to select a subset of relevant variables for a particular learning algorithm in order to improve the accuracy of prediction model and improve the efficiency of the model. We conduct an empirical analysis to evaluate and compare seven well-known variable selection techniques for multiple linear regression model, which is one of the most commonly used regression model in practice. The variable selection techniques we apply are forward selection, backward elimination, stepwise selection, genetic algorithm (GA), ridge regression, lasso (Least Absolute Shrinkage and Selection Operator) and elastic net. Based on the experiment with 49 regression data sets, it is found that GA resulted in the lowest error rates while lasso most significantly reduces the number of variables. In terms of computational efficiency, forward/backward elimination and lasso requires less time than the other techniques.

• 응용통계연구
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• 제21권3호
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• pp.441-453
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• 2008

프로스포츠 선수들의 연봉은 선수들의 개인 성적과 팀에 대한 기여도 등으로 결정된다는 가정하에 프로농구와 프로야구 선수들의 전년도 성적으로 다음해 연봉을 예측 분석하였다. 분석에 있어서 data visualization 기법을 통해 변수사이의 관계, 이상점 발견, 모형진단등을 하였다. 다중선형회귀 모형(Multiple Linear Regression)과 트리모형(Regression Tree)을 이용해서 자료를 분석하고 모델간 비교를 했으며, Cross-Validation을 이용해서 최적모델을 선택하였다. 특히, 자동으로 변수선택을 하는 stepwise regression방법을 그냥 사용하기보다는 먼저 설명변수들 사이의 관계나 설명변수와 반응변수 사이의 관계등을 조사하고 나서 이를 통해 선택된 변수들을 가지고 stepwise regression과 regression tree 방법론을 이용해서 적절한 변수 및 최종 모형을 선택하였다. 분석결과, 프로농구의 경우에는 경기당 득점, 어시스트, 자유투 성공수, 경력 등이 중요한 변수였고, 프로야구 투수의 경우에는 경력, 9이닝 당 삼진 수, 방어율, 피홈런 수 등이 중요한 변수였고, 프로야구 타자의 경우에는 경력, 안타 수, FA(자유계약)유무 여부 등이 중요한 변수였다.

• 한국항해항만학회지
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• 제44권3호
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• pp.187-194
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• 2020

항만의 주요 정책 및 향후 운영계획 수립 시 정확한 물동량 예측에 관한 연구는 매우 중요하며 이러한 중요성으로 인해 관련 연구가 활발히 수행되고 있다. 본 논문에서는 국내 최대 석탄 및 철광석 처리 항만인 광양항을 대상으로 단계적 회귀분석과 인공신경망모형을 활용하여 모형간 예측력을 비교하였다. 2009년 1월부터 2019년 1월까지 총 121개월의 월별자료를 활용하였으며 석탄 및 철광석 물동량에 영향을 주는 요인을 선정하여 공급관련요인과 시장·경제관련요인으로 분류하였다. 단계적 회귀분석 결과, 광양항 석탄 물동량 예측모형의 경우, 입항선박 톤수, 석탄가격 및 대미환율이 최종변수로 선정되었고 철광석 물동량 예측모형의 경우, 입항선박 톤수, 철광석가격이 최종변수로 선정되었다. 인공신경망모형의 경우, 모델 성능에 영향을 미치는 다양한 Hyper-parameters를 조정하며 최적 모델을 선정하는 시행착오법을 사용하였다. 분석결과 인공신경망모형이 단계적 회귀분석에 비해 우수한 예측성능을 나타내었으며 예측 모형별 예측값과 실측값을 그래프 상 비교 시에도 인공신경망모형이 단계적 회귀분석에 비해 고·저점을 유사하게 나타냈다.

• 대한간호학회지
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• 제33권5호
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• pp.625-632
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• 2003

Purpose: The purpose of this study was to assess the importance and sensitivity to nursing interventions of four nursing sensitive nursing outcomes selected from the Nursing Outcomes Classification (NOC). Outcomes for this study were 'Knowledge: Diet', 'Knowledge: Disease Process', 'Knowledge: Energy Conservation', and 'Knowledge: Health Behaviors'. Method: Data were collected from 183 nurses working in 2 university hospitals. Fehring method was used to estimate outcome and indicators' content and sensitivity validity. Multiple and stepwise regression were used to evaluate relationships between each outcome and its indicators. Result: Results confirmed the importance and nursing sensitivity of outcomes and their indicators. Key indicators of each outcomes were found by multiple regression. 'Knowledge: Diet' was suggested for adding new indicators because the variance explained by indicators was relatively low. Not all of the indicators selected for stepwise regression model were rated for highly in Fehring method. The R² statistics of the stepwise regression models were between 18 and 63% in importance by selected indicators and between 34 and 68% in contribution by selected indicators. Conclusion: This study refined what outcomes and indicators will be useful in clinical practice. Further research will be required for the revision of outcome and indicators of NOC. However, this study refined what outcomes and indicators will be useful in clinical practice.