• Title/Summary/Keyword: multiple regression analysis

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Simplification of PMV through Multiple Regression Analysis (다중회귀분석을 통한 PMV 모델의 단순화)

  • Moon, Yong-Jun;Noh, Kwang-Chul;Oh, Myung-Do
    • Korean Journal of Air-Conditioning and Refrigeration Engineering
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    • v.19 no.11
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    • pp.761-769
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    • 2007
  • The purpose of this study is to present a simplified model of predicted mean vote (PMV) using multiple regression analysis. We performed the experiments and the numerical calculations in the lecture room during summer and winter to simplify PMV. And the multiple regression analysis on PMV was conducted to estimate the contribution of independent variables toward PMV. From the multiple regression analysis, we found that the effect of independent variables on PMV followed in order, clo value>air temperatur>air velocity>mean radiant temperature>relative humidity. And the simplified PMV was proposed through a few assumptions and then was compared with original PMV. They had a good agreement with each other. Additionally, we compared the simplified PMV with EDT. We expected that the simplified PMV can be more useful than EDT to evaluate the thermal comfort in the place, where radiation is dominant. But the comfort range of the simplified PMV should be adjusted to predict the exact thermal comfort in the future.

The Geometry Prediction of Back-bead in Arc Welding

  • Lee, Jeong-Ick;Koh, Byung-Kab
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.16 no.5
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    • pp.84-89
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    • 2007
  • This research was done on the basis of assumption that there is a relationship between welding parameters and geometry of the back-bead being a gap in arc welding. Multiple regression analysis was used as method for predicting the geometry of the back-bead. The analysis data and the verification data were used for the formation of multiple regression analysis. The method was used to perform the prediction of the back-bead.

Correlation Analysis between Climate and Contamination Degree through Multiple Regression Analysis (다중회귀 분석을 통한 기후 및 오손도 간의 상관관계 분석)

  • Kim, Do-Young;Lee, Won-Young;Shim, Kyu-Il;Han, Sang-Ok;Park, Kang-Sik
    • Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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    • 2003.05e
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    • pp.49-52
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    • 2003
  • The performance of insulators under contaminated conditions is the underlying and the most factor that determines insulation design for outdoor applications, Among the contamination factors, The sea salt is the most dangerous factor, and the salt factor have closed relation with climatic conditions, such as wind, temperature, humidity and so on, Effect of these factors to insulation system is different of each other, and need to show the correlation by multiple regression analysis techniques. In this paper, predicted and analyzed equivalent salt deposit density (ESDD) by change climatic condition through multiple regression analysis.

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Study on the Annoyance Response in the Area Exposed by Road Traffic Noise and Railway Noise (도로교통소음과 철도소음 복합노출지역에서의 성가심 반응)

  • Ko, Joon-Hee;Chang, Seo-Il;Son, Jin-Hee;Lee, Kun
    • Transactions of the Korean Society for Noise and Vibration Engineering
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    • v.20 no.2
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    • pp.172-178
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    • 2010
  • The multiple regression analysis and path analysis in each dominant area of noise source are conducted to analyze the relationship between dependent variables like annoyance and independent ones such as noise and non-noise factors. The multiple regression analysis shows that impact of noise factors is the highest to annoyance in dominant areas of road traffic and railway noise. Meanwhile, impact of non-noise factors such as sensitivity and satisfaction of environment on annoyance is also high in these areas. The path analysis result for multivariate analysis between various independent and dependent variables is similar to that of the multiple regression analysis. However, noise factor is the greatest factor influent on annoyance in the dominant areas of the combined noise, and relationship between annoyance and sensitivity is the highest in combined area exposed to road traffic noise and railway noise.

A Study on Forecast of Oyster Production using Time Series Models (시계열모형을 이용한 굴 생산량 예측 가능성에 관한 연구)

  • Nam, Jong-Oh;Noh, Seung-Guk
    • Ocean and Polar Research
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    • v.34 no.2
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    • pp.185-195
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    • 2012
  • This paper focused on forecasting a short-term production of oysters, which have been farmed in Korea, with distinct periodicity of production by year, and different production level by month. To forecast a short-term oyster production, this paper uses monthly data (260 observations) from January 1990 to August 2011, and also adopts several econometrics methods, such as Multiple Regression Analysis Model (MRAM), Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, and Vector Error Correction Model (VECM). As a result, first, the amount of short-term oyster production forecasted by the multiple regression analysis model was 1,337 ton with prediction error of 246 ton. Secondly, the amount of oyster production of the SARIMA I and II models was forecasted as 12,423 ton and 12,442 ton with prediction error of 11,404 ton and 11,423 ton, respectively. Thirdly, the amount of oyster production based on the VECM was estimated as 10,425 ton with prediction errors of 9,406 ton. In conclusion, based on Theil inequality coefficient criterion, short-term prediction of oyster by the VECM exhibited a better fit than ones by the SARIMA I and II models and Multiple Regression Analysis Model.

Evaluation of Taste in Kanjang Made with Barley Bran Using Multiple Regression Analysis (중회귀분석을 이용한 보리간장 맛의 평가)

  • Choi, Ung-Kyu;Park, June-Hong
    • Korean Journal of Food Science and Technology
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    • v.36 no.1
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    • pp.75-80
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    • 2004
  • This research was conducted to predict taste of barley kanjang using multiple regression analysis between taste components and sensory score. In the analysis of single correlation, the correlation coefficient of proline, alanine, Methionine, lysine, histidine, lavulinic acid, ${\alpha}$-ketogutaric acid was significant in 5% level. On the other hand, the taste of barley kanjang was not significantly effected by threonine, serine, cystein, phenylalanine, succinic acid, arabinose, xylose, and sucrose. It was impossible to measure taste of kanjang with barley bran to use simple correlation analysis. The 93% of barley kanjang taste was predicted using multiple regression analysis with taste components and sensory evaluation scores.

Flash Point Measurement of n-Propanol+n-Hexanol and n-Butanol+n-Hexanol Systems Using Seta Flash Closed Cup Tester (Seta Flash 밀폐식 장치를 이용한 n-Propanol+n-Hexanol계와 n-Butanol+n-Hexanol계의 인화점 측정)

  • Ha, Dong-Myeong;Lee, Sungjin
    • Journal of the Korean Society of Safety
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    • v.34 no.1
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    • pp.34-39
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    • 2019
  • Flash point is the important indicator to determine fire and explosion hazards of liquid solutions. In this study, flash points of n-propanol+n-hexanol and n-butanol+n-hexanol systems were obtained by Seta flash tester. The methods based on UNIFAC equation and multiple regression analysis were used to calculate flash point. The calculated flash point was compared with the experimental flash point. Absolute average errors of flash points calculated by UNIFAC equation are $2.9^{\circ}C$ and $0.6^{\circ}C$ for n-propanol+n-hexanol and n-butanol+n-hexanol, respectively. Absolute average errors of flash points calculated by multiple regression analysis are $0.5^{\circ}C$ and $0.2^{\circ}C$ for n-propanol+ n-hexanol and n-butanol+n-hexanol, respectively. As can be seen from AAE, the values calculated by multiple regression analysis are noticed to be better than the values by the method based on UNIFAC eauation.

Forecasting Technique of Downstream Water Level using the Observed Water Level of Upper Stream (수계 상류 관측 수위자료를 이용한 하류 홍수위 예측기법)

  • Kim, Sang Mun;Choi, Byungwoong;Lee, Namjoo
    • Ecology and Resilient Infrastructure
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    • v.7 no.4
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    • pp.345-352
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    • 2020
  • Securing the lead time for evacuation is crucial to minimize flood damage. In this study, downstream water levels for heavy rainfall were predicted using measured water level observation data. Multiple regression analysis and artificial neural networks were applied to the Seom River experimental watershed to predict the water level. Water level observation data for the Seom River experimental watershed from 2002 to 2010 were used to perform the multiple regression analysis and to train the artificial neural networks. The water level was predicted using the trained model. The simulation results for the coefficients of determination of the artificial neural network level prediction ranged from 0.991 to 0.999, while those of the multiple regression analysis ranged from 0.945 to 0.990. The water level prediction model developed using an artificial neural network was better than the multiple-regression analysis model. This technique for forecasting downstream water levels is expected to contribute toward flooding warning systems that secure the lead time for streams.

ALC(Autoclaved Lightweight Concrete) Hardness Prediction by Multiple Regression Analysis (다중회귀분석을 이용한 ALC 경도예측에 관한 연구)

  • Kim, Kwang-Soo;Baek, Seung-Hoon;Chung, Soon-Suk
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.7 no.2
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    • pp.101-111
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    • 2012
  • In the ALC(Autoclaved lightweight concrete) manufacturing process, if the pre-cured semi-cake is removed after proper time is passed, it will be hard to retain the moisture and be easily cracked. Therefore, in this research, we took the research by multiple regression analysis to find relationship between variables for the prediction the hardness that is the control standard of the removal time. We study the relationship between Independent variables such as the V/T(Vibration Time), V/T movement, expansion height, curing time, placing temperature, Rising and C/S ratio and the Dependent variables, the hardness by multiple regression analysis. In this study, first, we calculated regression equation by the regression analysis, then we tried phased regression analysis, best subset regression analysis and residual analysis. At last, we could verify curing time, placing temperature, Rising and C/S ratio influence to the hardness by the estimated regression equation.

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Prediction of Jominy Hardness Curves Using Multiple Regression Analysis, and Effect of Alloying Elements on the Hardenability (다중 회귀 분석을 이용한 보론강의 조미니 경도 곡선 예측 및 합금 원소가 경화능에 미치는 영향)

  • Wi, Dong-Yeol;Kim, Kyu-Sik;Jung, Byoung-In;Lee, Kee-Ahn
    • Korean Journal of Materials Research
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    • v.29 no.12
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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.