• 패션비즈니스
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• 제10권4호
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• pp.140-150
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• 2006

The purpose of this study was to examine affecting the addictive buying behavior toward fashion luxury products. 227 female college students were who purchased fashion luxury products surveyed for this study. For data analysis, descriptive statistics, factor analysis, and multiple regression were used. As the results, addictive buying toward luxury products was classified into three factors: impulse addictive, money addictive, and psychological addictive. Also, consumers' individuality pursuit was classified into four factors: unique choice, non-similarity choice, individual choice and non-social interest. Multiple regression results revealed that impulse buying, stress, and unique choice accounted for 38% of the explained variance in addictive buying toward luxury products. Also, regression results indicated that impulse buying, stress, unique choice and reference group accounted for 38% of the explained variance in impulse addictive buying. Finally, regression results pointed out that unique choice and impulse accounted 24% of the explained variance in psychological addictive buying. Based on these results, fashion social responsibility marketing strategies would be suggested.

• 한국농공학회지
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• 제30권4호
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• pp.45-61
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• 1988

The purpose of this study is to develop regression equations between annual specific sedi- ment of reservoirs and physiographic factors of watersheds. 122 irrigation reservoirs, which have irrigation areas equal to or larger than 200 ha, located in Korea except Cheju province are used in the analysis. Simple regression analyses between the specific annual sediment and each of the physical characteristic factors of the reservoirs are carried out at first. Then, multiple regression analyses between the annual specific sediment and the physical characteristic factors with high correlation coefficients in the simple regression analyses are made. The results obtained from this study are as follows : 1. The results of the sirnple regression analyses show that in each province the watershed area, the length of mainstream, the circumferential length of watershed have high cor- relation coefficients (R=0.814-0.986), and that drainage density, reservoir capacity per watershed area, drainage frequency, basin relief have low correlation coefficients (R=0. 387-0.955). 2. The purposed multiple regression equations between the annual specific sediment of reservoirs and three major characteritic factors of watersheds, namely, the watershed area, the circumferential length of watershed, and the length of mainstream, are proposed as given in Table 2. 3. The result of the simple regression analyses with respect to the reservoir elevation except Jeonnam province, which has very different characteristics comparing to other provinces, shows that watershed area, main stream length and circumferential length have high correlation coefficients (R=0.806-0.884) in low-elevation reservoirs and intermediate- elevation reservoirs, but low correlation coefficients (R=0.639-0.739) in high-elevation reservoirs. 4. With respect to the reservoir elevation, the proposed multiple regression equations bet- ween the annual specific sediment of reservoirs and the three major characteristic factors of watershed which have high correlation coefficients are proposed as given in Table 5.

• 한국인터넷방송통신학회논문지
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• 제19권6호
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• pp.219-225
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• 2019

한자 급수와 같이 기존 한자 난이도 선정 방식에 문제점이 있다. 실생활에서 쓰이는 한글 단어와 차이가 나며 해당 급수가 실제로 얼마나 많이 쓰이는지 알 수가 없다. 이러한 문제를 해결하기 위해 빈도수를 이용하여 다중 회귀 분석을 이용하여 한자 난이도를 측정한다. 초등 교과서를 기반으로 한자활용빈도수와 한글의미빈도수를 집계한다. 두 빈도수와 획수를 함께 사용하여 설문지를 작성하여 해당 한자의 학습 적정 시기를 답변 받아 이를 회귀에서 사용할 타겟 변수로 이용한다. 단계별 회귀분석을 이용하여 적절한 피처를 선택하고 다중 선형 회귀 분석을 한다. 모델의 R²는 0.1105가 나왔으며 RMSE는 0.1105의 결과가 나왔다.

• Journal of Animal Science and Technology
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• 제66권4호
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• pp.834-845
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• 2024

Currently, in pork auctions in Korea, only carcass weight and backfat thickness provide information on meat quantity, while the production volume of primal cuts and fat contents remains largely unknown. This study aims to predict the production of primal cuts in pigs and investigate how these carcass traits affect pricing. Using the VCS2000, the production of shoulder blade, loin, belly, shoulder picnic, and ham was measured for gilts (17,257 pigs) and barrows (16,365 pigs) of LYD (Landrace × Yorkshire × Duroc) pigs. Single and multiple regression analysis were conducted to analyze the relationship between the primal cuts and carcass weight. The study also examined the correlation between each primal cut, backfat thickness (1st thoracic vertebra backfat thickness, grading backfat thickness, and Multi-brached muscle middle backfat thickness), pork belly fat percentage, total fat yield, and auction price. A multiple regression analysis was conducted between the carcass traits that showed a high correlation and the auction price. After conducting a single regression analysis on the primal cuts of gilt and barrow, all coefficients of determination (R²) were 0.77 or higher. In the multiple regression analysis, the R² value was 0.98 or higher. The correlation coefficient between the carcass weights and the auction price exceeded 0.70, while the correlation coefficients between the primal cuts and the auction prices were above 0.65. In terms of fat content, the backfat thickness of gilt exhibited a correlation coefficient of 0.70, and all other items had a correlation coefficient of 0.47 or higher. The correlation coefficients between the Forequarter, Middle, and Hindquarter and the auction price were 0.62 or higher. The R² values of the multiple regression analysis between carcass traits and auction price were 0.5 or higher for gilts and 0.4 or higher for barrows. The regression equations between carcass weight and primal cuts derived in this study exhibited high determination coefficients, suggesting that they could serve as reliable means to predict primal cut production from pig carcasses. Elucidating the correlation between primal cuts, fat contents and auction prices can provide economic indicators for pork and assist in guiding the direction of pig farming.

• Journal of Mechanical Science and Technology
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• 제17권12호
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• pp.1969-1976
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making for the product concepts, and the best alternative can be selected based on its estimated LCA and benefits. Both the lack of detailed information and time for a full LCA for a various range of design concepts need a new approach for the environmental analysis. This paper explores a new approximate LCA methodology for the product concepts by grouping products according to their environmental characteristics and by mapping product attributes into environmental impact driver (EID) index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then, a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for newly designed products. The training is generalized by using product attributes for an EID in a group as well as another product attributes for the other EIDs in other groups. The neural network model with back propagation algorithm is used, and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines for the design of environmentally conscious products in conceptual design phase.

• 한국의류산업학회지
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• 제1권2호
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• pp.109-118
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• 1999

The purpose of this study is to investigate the effects of clothing shopping orientations on consumers emotions in clothing stores based on level of clothing involvement. For this purpose, factor analysis was used to identify shopper types by clothing shopping orientation and factors of consumers' emotions and multiple regression analysis was used in each level of upper 25% and lower 25% of clothing involvement to find out the effects of clothing shopping orientations on consumers' emotions. The results of this study can be summarized as follows; 1. 4 factors were found in clothing involvement. 2. 6 factors were found in clothing shopping orientations but 'recreational shopping orientation' 'economic shopping orientation' of high loading factors were used for multiple regression analysis, 3. 4 factors were found in consumers' emotions but 'pleasure' arousal' 'enervation' were selected for multiple regression analysis. 4. In the upper 25% group of clothing involvement level; it is founded that 'recreational shopping orientation' influenced 'pleasure' and 'economic shopping orientation' did not influence 'pleasure'; it is founded that 'recreational shopping orientation' influenced 'arousal' and 'economic shopping orientation' did not influence 'arousal'; 'recreational shopping orientation' and 'economic shopping orientation' did not influence 'enervation'. 5, In the lower 25% group of clothing involvement level; it is founded that 'recreational shopping orientation' and 'economic shopping orientation' influenced 'pleasure' and did not influence 'arousal'; it is founded that only 'economic shopping orientation' influenced 'enervation' negatively.

• 한국융합학회논문지
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• 제10권7호
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• pp.1-6
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• 2019

현대사회는 1인가구가 증가함에 따라 불규칙한 식습관으로 인해 영양이 불균형하게 포진되어있다. 이러한 식습관은 위장질환, 소화기 질환 등 만성질환의 발병률을 증가시켰다. 본 논문은 위장질환 예방을 위한 다중회귀분석을 이용한 식이지식 예측을 제안한다. 제안하는 방법은 식이지식 예측을 통해 사용자의 위장질환과 식이영양을 관리하는 방법이다. 헬스 플랫폼에서 스마트 기기를 통해 수집된 사용자의 PHR을 통합한다. 통합된 데이터로부터 다중회귀분석을 이용하여 사용자의 식이와 활동량 변화를 분석한다. 사용자의 식이 성분과 소모 칼로리, 기초대사와 같은 상황정보를 입력으로 적절한 식이성분, 위장질환 수치의 변화를 예측하고 필요할 것으로 나타나는 영양성분을 사용자에게 권장한다. 이를 통해 현대인들은 균형 잡힌 식사를 통해 위장질환을 관리할 수 있다.

• Journal of applied mathematics & informatics
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• 제26권3_4호
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• pp.679-688
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• 2008

In multivariate regression analysis there exist many influence measures on the regression estimates. However it seems to be few of influence diagnostics on test statistics in hypothesis testing. Case-deletion approach is fundamental for investigating influence of observations on estimates or statistics. Tang and Fung (1997) derived single case-deletion of the Wilks' ratio, Lawley-Hotelling trace, Pillai's trace for testing a general linear hypothesis of the regression coefficients in multivariate regression. In this paper we derived more extended form of those measures to deal with joint influence among observations. A numerical example is given to illustrate the effect of joint influence on the test statistics.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.302-305
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• 2004

The aim of this study is to apply and crossvalidate a spatial logistic multiple-regression model at Boun, Korea, using a Geographic Information System (GIS). Landslide locations in the Boun area were identified by interpretation of aerial photographs and field surveys. Maps of the topography, soil type, forest cover, geology, and land-use were constructed from a spatial database. The factors that influence landslide occurrence, such as slope, aspect, and curvature of topography, were calculated from the topographic database. Texture, material, drainage, and effective soil thickness were extracted from the soil database, and type, diameter, and density of forest were extracted from the forest database. Lithology was extracted from the geological database and land-use was classified from the Landsat TM image satellite image. Landslide susceptibility was analyzed using landslide-occurrence factors by logistic multiple-regression methods. For validation and cross-validation, the result of the analysis was applied both to the study area, Boun, and another area, Youngin, Korea. The validation and cross-validation results showed satisfactory agreement between the susceptibility map and the existing data with respect to landslide locations. The GIS was used to analyze the vast amount of data efficiently, and statistical programs were used to maintain specificity and accuracy.

• Computers and Concrete
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• 제16권3호
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• pp.399-414
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• 2015

Nowadays, fiber reinforced polymer (FRP) composites are widely used for rehabilitation, repair and strengthening of reinforced concrete (RC) structures. Also, recent advances in concrete technology have led to the production of high strength concrete, HSC. Such concrete due to its very high compression strength is less ductile; so in seismic areas, ductility is an important factor in design of HSC members (especially FRP strengthened members) under flexure. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and multiple regression analysis are used to predict the curvature ductility factor of FRP strengthened reinforced HSC (RHSC) beams. Also, the effects of concrete strength, steel reinforcement ratio and externally reinforcement (FRP) stiffness on the complete moment-curvature behavior and the curvature ductility factor of the FRP strengthened RHSC beams are evaluated using the analytical approach. Results indicate that the predictions of ANFIS and multiple regression models for the curvature ductility factor are accurate to within -0.22% and 1.87% error for practical applications respectively. Finally, the effects of height to wide ratio (h/b) of the cross section on the proposed models are investigated.