• 한국식품과학회지
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• 제36권1호
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• pp.75-80
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• 2004

본 연구에서는 맛성분과 관능검사자료를 중회귀 분석하여 보리간장 맛을 평가하였다. 단순상관에서는 proline, alanine, methionine, lysine, histidine, lavulinic acid, ${\alpha}$-ketogutaric acid 및 citric acid와 관능검사 점수사이의 상관계수가 유의성이 높은 것으로 나타났으며, threonine, serine, cystein, phenylalanine, succinic acid, arabinose, xylose 및 sucrose 둥의 성분은 관능검사 점수에 거의 상관이 없는 것으로 나타났다. 하지만 단순상관분석으로는 보리간장 맛의 품질을 측정하는 것이 불가능하였다. 중회귀 분석을 실시한 결과 결정계수가 0.93 이상을 나타내어 맛성분 함량과 중회귀식을 이용하여 보리간장 맛의 93%를 추정하는 것이 가능하였다.

• 한국환경과학회지
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• 제16권11호
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• pp.1301-1312
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• 2007

The objective of this study is to propose a critical storm duration forecasting model on storm runoff in small river basin. The critical storm duration data of 582 sub-basin which introduced disaster impact assessment report on the National Emergency Management Agency during the period from 2004 to 2007 were collected, analyzed and studied. The stepwise multiple regression method are used to establish critical storm duration forecasting models(Linear and exponential type). The results of multiple regression analysis discriminated the linear type more than exponential type. The results of multiple linear regression analysis between the critical storm duration and 5 basin characteristics parameters such as basin area, main stream length, average slope of main stream, shape factor and CN showed more than 0.75 of correlation in terms of the multi correlation coefficient.

• 대한임베디드공학회논문지
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• 제17권4호
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• pp.249-255
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• 2022

The COVID-19 virus appeared in 2019 and is extremely contagious. Because it is very infectious and has a huge impact on people's mobility. In this paper, multiple linear regression and random forest models are used to predict the number of COVID-19 cases using COVID-19 infection status data (open source data provided by the Ministry of health and welfare) and Google Mobility Data, which can check the liquidity of various categories. The data has been divided into two sets. The first dataset is COVID-19 infection status data and all six variables of Google Mobility Data. The second dataset is COVID-19 infection status data and only two variables of Google Mobility Data: (1) Retail stores and leisure facilities (2) Grocery stores and pharmacies. The models' performance has been compared using the mean absolute error indicator. We also a correlation analysis of the random forest model and the multiple linear regression model.

• 수산해양기술연구
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• 제60권1호
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• pp.57-70
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• 2024

In order to present a predictive drift model, Jeju National University's training ship was tested for about 11 hours and 40 minutes, and 81 samples that selected one of the entire samples at ten-minute intervals were subjected to regression analysis after verifying outliers and influence points. In the outlier and influence point analysis, although there is a part where the wind direction exceeds 1 in the DFBETAS (difference in Betas) value, the CV (cumulative variable) value is 6%, close to 1. Therefore, it was judged that there would be no problem in conducting multiple regression analyses on samples. The standard regression coefficient showed how much current and wind affect the dependent variable. It showed that current speed and direction were the most important variables for drift speed and direction, with values of 47.1% and 58.1%, respectively. The analysis showed that the statistical values indicated the fit of the model at the significance level of 0.05 for multiple regression analysis. The multiple correlation coefficients indicating the degree of influence on the dependent variable were 83.2% and 89.0%, respectively. The determination of coefficients were 69.3% and 79.3%, and the adjusted determination of coefficients were 67.6% and 78.3%, respectively. In this study, a more quantitative prediction model will be presented because it is performed after identifying outliers and influence points of sample data before multiple regression analysis. Therefore, many studies will be active in the future by combining them.

• 한국재료학회지
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• 제29권12호
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• pp.781-789
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• 2019

The prediction of Jominy hardness curves and the effect of alloying elements on the hardenability of boron steels (19 different steels) are investigated using multiple regression analysis. To evaluate the hardenability of boron steels, Jominy end quenching tests are performed. Regardless of the alloy type, lath martensite structure is observed at the quenching end, and ferrite and pearlite structures are detected in the core. Some bainite microstructure also appears in areas where hardness is sharply reduced. Through multiple regression analysis method, the average multiplying factor (regression coefficient) for each alloying element is derived. As a result, B is found to be 6308.6, C is 71.5, Si is 59.4, Mn is 25.5, Ti is 13.8, and Cr is 24.5. The valid concentration ranges of the main alloying elements are 19 ppm < B < 28 ppm, 0.17 < C < 0.27 wt%, 0.19 < Si < 0.30 wt%, 0.75 < Mn < 1.15 wt%, 0.15 < Cr < 0.82 wt%, and 3 < N < 7 ppm. It is possible to predict changes of hardenability and hardness curves based on the above method. In the validation results of the multiple regression analysis, it is confirmed that the measured hardness values are within the error range of the predicted curves, regardless of alloy type.

• 한국농공학회논문집
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• 제65권3호
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• pp.15-28
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• 2023

The compaction properties of the soil change depending on the physical properties, and are also affected by crushing of the particles. Since the particle size distribution of soil affects the engineering properties of the soil, it is necessary to analyze the material properties to understand the compaction characteristics. In this study, the size of each sieve was classified into four in the particle size analysis as a material property, and the compaction characteristics were evaluated by multiple regression and maximum dry unit weight. As a result of maximum dry unit weight prediction, multiple regression analysis showed R² of 0.70 or more, and DNN analysis showed R² of 0.80 or more. The reliability of the prediction result analyzed by DNN was evaluated higher than that of multiple regression, and the analysis result of DNN-T showed improved prediction results by 1.87% than DNN. The prediction of maximum dry unit weight using particle size distribution seems to be applied to evaluate the compacting state by identifying the material characteristics of roads and embankments. In addition, the particle size distribution can be used as a parameter for predicting maximum dry unit weight, and it is expected to be of great help in terms of time and cost of applying it to the compaction state evaluation.

• 산업경영시스템학회지
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• 제38권1호
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• pp.143-151
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• 2015

In this paper, we investigate how the power consumption of a heat pump dryer depends on various factors in the drying process by analyzing variables that affect the power consumption. Since there are in general many variables that affect the power consumption, for a feasible analysis, we utilize the principal component analysis to reduce the number of variables (or dimensionality) to two or three. We find that the first component is correlated positively to the entrance temperature of various devices such as compressor, expander, evaporator, and the second, negatively to condenser. We then model the power consumption as a multiple regression with two and/or three transformed variables of the selected principal components. We find that fitted value from the multiple regression explains 80~90% of the observed value of the power consumption. This results can be applied to a more elaborate control of the power consumption in the heat pump dryer.

• Applied Biological Chemistry
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• 제45권1호
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• pp.18-24
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• 2002

본 연구는 보리등겨로 제조한 간장 맛의 특성을 찾기 위해서 수행되었다. 맛성분은 기기분석으로, 관능검사는 panel로, 그 외 통계적 처리의 방법 등을 이용하였다. 보리간장 맛성분은 유기산, 유리당 및 유리아미노산으로 분류하였으며, 이들과 관능검사 성적과의 단순상관으로 보리간장 맛의 품질을 결정하는 것은 불가능하였다. 중상관계수는 절대값의 대수 변환에서 가장 높게 나타났으며, 따라서 단계적 중회귀분석은 가장 설명력이 높으며, 표준오차가 적은 절대값의 대수 변환을 이용하여 실시하였다. 단계적 중회귀분석 결과, 보리간장 맛의 좋고 나쁨에 기여를 하는 성분은 짠맛, 구수한 맛 및 쓴맛을 내는 성분 순이었다.

• 디지털산업정보학회논문지
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• 제8권3호
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• pp.199-212
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• 2012

This study focuses on the influence of followership on leadership and empowerment, and to verify based on the control variables taken in IT SME's to enhance competitiveness through innovation and improvement plan that have been taken. Because there can be a lot of information to be taken, the laws of Moderated Regression Multiple analysis(MMR) were used. Amos, due to the moderating effect of Structural Equation Modeling(SEM) has been employed to re-verify the results seen with Moderated Regression Multiple analysis. The paper focuses on determining whether transformational leadership or transactional leadership is effective as shown by the levels of empowerment derived from these two types of leadership under study. As a result, both the Moderated Regression Multiple analysis and structural equation model searched information on transformational and followership for empowerment having moderating effects. In the Moderated Regression Multiple analysis, results showed that empowerment for leadership in business in the regulation of followership role appeared not to be seen. However, using the structural equation modeling, moderating effects have been found.

• 한국건축시공학회지
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• 제13권1호
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• pp.66-74
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• 2013

In the early stages of a construction project, the most important thing is to predict construction costs in a rational way. For this reason, many studies have been performed on the estimation of construction costs for apartment housing and office buildings at early stage using artificial intelligence, statistics, and the like. In this study, cost data held by a provincial Office of Education on elementary schools constructed from 2004 to 2007 were used to compare the multiple regression model with an artificial neural network model. A total of 96 historical data were classified into 76 historical data for constructing models and 20 historical data for comparing the constructed regression model with the artificial neural network model. The results of an analysis of predicted construction costs were that the error rate of the artificial neural network model is lower than that of the multiple regression model.