• Title, Summary, Keyword: Regression equation

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Development of Regression Equation for Water Quantity Estimation in a Tidal River (감조하천에서의 저수위 유량산정 다중회귀식 개발)

  • Lee, Sang Jin;Ryoo, Kyong Sik;Lee, Bae Sung;Yoon, Jong Su
    • Journal of Korean Society on Water Environment
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    • v.23 no.3
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    • pp.385-390
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    • 2007
  • Reliable flow measurement for dry season is very important to set up the in-stream flow exactly and total maximum daily load control program in the basin. Especially, in the points which tidal current effects are dominant because reliability of the low measurement decrease. The reliable measuring methods are needed. In this study, we analysis the water surface elevation difference of water surface elevation. Quantity relationship to consider tidal currents in these regions. It is known that tidal current effects from Nakdong river barrage are dominant in Samrangjin measuring station. We developed multiple regression equation with water surface elevation, quantity, and difference of water surface elevation and compared these results water measured rating curve. All of these regression equation including linear regression equation and log regression equation fits better measured data them existing water surface elevation quantity line and Among three equations, the log regression equation is best to represent the measured the rating curve in Samrangjin point. The log regression equation is useful method to obtain the quantity in the regions which tidal currents are dominant.

A Study on Stochastic Estimation of Monthly Runoff by Multiple Regression Analysis (다중회귀분석에 의한 하천 월 유출량의 추계학적 추정에 관한 연구)

  • 김태철;정하우
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.22 no.3
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    • pp.75-87
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    • 1980
  • Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

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Estimation of Pollutant Loads Delivery Ratio by Flow Duration Using Regression Equation in Hwangryong A Watershed (회귀식을 이용한 황룡A 유역에서의 유황별 유달율 산정)

  • Jung, Jae-Woon;Yoon, Kwang-Sik;Joo, Seuk-Hun;Choi, Woo-Young;Lee, Yong-Woon;Rhew, Doug-Hee;Lee, Su-Woong;Chang, Nam-Ik
    • Journal of The Korean Society of Agricultural Engineers
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    • v.51 no.6
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    • pp.25-31
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    • 2009
  • In this study, pollutant loads delivery ratio by flow duration in Hwangryoung A watershed was estimated. The delivery ratio was estimated with measured data by Ministry of Environment(MOE) and the regression equation based on geomorphic parameters. Eight day interval flow data measured by the MOE were converted to daily flow to calculate daily load and flow duration curve by correlating data of neighboring station which has daily flow data. Regression equation developed by previous study was tested to study watershed and found to be satisfactory. The delivery ratios estimated by two methods were compared. For the case of Biochemical oxygen demand(BOD), the delivery ratios of low flow condition were 7.6 and 15.5% by measured and regression equation, respectively. Also, the delivery ratios of Total phosphorus(T-P) for normal flow condition were 13.3 and 6.3% by measured and regression equation, respectively.

Uncertainty of Efficiency Equation of Solar Thermal Collectors (태양열 집열기 효율식의 불확도)

  • Lee, Kyoung-Ho;Lee, Soon-Myung
    • 한국신재생에너지학회:학술대회논문집
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    • pp.65.1-65.1
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    • 2010
  • Thermal performance tests of solar thermal collectors include determination of coefficient parameters in an efficiency equation. The parameters can be estimated using regression method to minimize an objective function as sum of differences between measured efficiency data and regressed efficiency equation. However, this conventional approach doesn't consider measurement uncertainties. In this presentation, a method to determine regression parameters in the efficiency equation and uncertainties of the parameters is described with mainly mathematical expressions based on literature reviews. In the method, parameters in the equation for collector efficiency can be determined using regression analysis with a weighting factor in the objective function. The weighting factor can be uncertainties of the differences between measured and fitted efficiencies. To evaluate the approach, performance estimation of a solar collector using the efficiency equation with uncertainties is compared to the result using the conventional efficiency equation by a simulated way for a case in one of previous studies.

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Quantitative Analysis by Diffuse Reflectance Infrared Fourier Transform and Linear Stepwise Multiple Regression Analysis I -Simultaneous quantitation of ethenzamide, isopropylantipyrine, caffeine, and allylisopropylacetylurea in tablet by DRIFT and linear stepwise multiple regression analysis-

  • Park, Man-Ki;Yoon, Hye-Ran;Kim, Kyoung-Ho;Cho, Jung-Hwan
    • Archives of Pharmacal Research
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    • v.11 no.2
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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.

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Development of Correction Equation and Characteristics Evaluation for Moisture Meter of Microwave Resistance Type (고주파 저항방식 함수율계의 보정식 개발 및 특성평가)

  • Jeon, Hong-Young;Kang, Tae-Hwann;Han, Chung-Su
    • Journal of Biosystems Engineering
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    • v.35 no.3
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    • pp.175-181
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    • 2010
  • This study compared moisture content measured by moisture meter of microwave resistance type(MMMRT) and standard moisture content of paddy, and developed the correction equation using linear and curvilinear regression analysis, and to explore its significance test. The correction factor according to the range of moisture content was developed to improve the measurement precision of MMMRT. The results were as followings. The coefficients of determination of correction equation by linear and curvilinear regression analysis with comparing the MMMRT and standard moisture content were 0.946 and 0.968, respectively. The moisture content error of MMMRT and standard moisture content measured after the MMMRT were corrected by moisture content rate of every 5% using the correction equation by curvilinear regression analysis appeared with 0~0.5% and 0.9~1.8% respectively in the moisture content range of 15~20% and 20~25%.

Prediction of Surface Roughness of Al7075 on End-Milling Working Conditions by Non-linear Regression Analysis (비선형 회귀분석에 의한 엔드밀 가공조건에 따른 Al7075의 표면정도 예측)

  • Cho, Yon-Sang;Park, Heung-Sik
    • Tribology and Lubricants
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    • v.26 no.6
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    • pp.329-335
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    • 2010
  • Recently, the End-milling processing is needed the high-precise technique to get a good surface roughness and rapid time in manufacturing of precision machine parts and electronic parts. The optimum surface roughness has an effect on end-milling working condition such as, cutting direction, spindle speed, feed rate and depth of cut, and so on. It needs to form the correlation of working conditions and surface roughness. Therefore this study was carried out to presume of surface roughness on end-milling working condition of Al7075 by regression analysis. The results was shown that the coefficient of determination($R^2$) of regression equation had a fine reliability of 87.5% and nonlinear regression equation of surface rough was made by multiple regression analysis.

Predictive analyses for balance and gait based on trunk performance using clinical scales in persons with stroke

  • Woo, Youngkeun
    • Physical Therapy Rehabilitation Science
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    • v.7 no.1
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    • pp.29-34
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    • 2018
  • Objective: This study aimed to predict balance and gait abilities with the Trunk Impairment scales (TIS) in persons with stroke. Design: Cross-sectional study. Methods: Sixty-eight participants with stoke were assessed with the TIS, Berg Balance scale (BBS), and Functional Gait Assessment (FGA) by a therapist. To describe of general characteristics, we used descriptive and frequency analyses, and the TIS was used as a predictive variable to determine the BBS. In the simple regression analysis, the TIS was used as a predictive variable for the BBS and FGA, and the TIS and BBS were used as predictive variables to determine the FGA in multiple regression analysis. Results: In the group with a BBS score of >45 for regression equation for predicting BBS score using TIS score, the coefficient of determination ($R^2$) was 0.234, and the $R^2$ was 0.500 in the group with a BBS score of ${\leq}45$. In the group with an FGA score >15 for regression equation for predicting FGA score using TIS score, the $R^2$ was 0.193, and regression equation for predicting FGA score using TIS score, the $R^2$ was 0.181 in the group of FGA score ${\leq}15$. In the group of FGA score >15 for regression equation for predicting FGA score using TIS and BBS score, the $R^2$ was 0.327. In the group of FGA score ${\leq}15$ for regression equation for predicting FGA score using TIS and BBS score, the $R^2$ was 0.316. Conclusions: The TIS scores are insufficient in predicting the FGA and BBS scores in those with higher balance ability, and the BBS and TIS could be used for predicting variables for FGA. However, TIS is a strong predictive variable for persons with stroke who have poor balance ability.

Development of Multiple Regression Equation for Estimation of Suspended Solids in Unmeasurable Watershed (미계측 유역의 부유물질 산정을 위한 다중회귀식 개발)

  • Choi, Han-Kyu;Park, Jae-Yong;Park, Soo-Jin
    • Journal of Industrial Technology
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    • v.26 no.A
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    • pp.119-127
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    • 2006
  • The purpose of this study is to present quantitatively the influence of variables that had the largest effect on the changes in suspended solids(SS), which would cause turbid water phenomenon, among water quality factors of the non-point pollution source, and then to develop a multiple regression equation of SS and predict the water quality of ungaged watersheds so as to provide basic data to establish efficient management plans for SS which flow in rivers and lakes. To identify the correlation of SS with the amount of rainfall and the state of land use, a simple correlation analysis and a simple regression analysis were conducted respectively. Finally, a multiple regression analysis was conducted to provide that SS were set as dependent variables while the amount of rainfall, paddy fields and dry fields were set as independent variables. As a result, the amount of rainfall had the most significant influence on changes in SS, followed by dry fields and paddy fields. In addition, the multiple regression equation was developed to predict SS in unmeasurable watersheds.

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A Generalized Calorie Estimation Algorithm Using 3-Axis Accelerometer

  • Choi, Jee-Hyun;Lee, Jeong-Whan;Shin, Kun-Soo
    • Journal of Biomedical Engineering Research
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    • v.27 no.6
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    • pp.301-309
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    • 2006
  • The main purpose of this study is to derive a regression equation that predicts the individual differences in activity energy expenditure (AEE) using accelerometer during different types of activity. Two subject groups were recruited separately in time: One is a homogeneous group of 94 healthy young adults with age ranged from $20\sim35$ yrs. The other subject group has a broad spectrum of physical characteristics in terms of age and fat ratio. 226 adolescents and adults of age ranged from $12\sim57$ yrs and fat ratio from $4.1\sim39.7%$ were in the second group. The wireless 3-axis accelerometers were developed and carefully fixed at the waist belt level. Simultaneously the total calorie expenditure was measured by gas analyzer. Each subject performed walking and running at speeds of 1.5, 3.0, 4.5, 6.0, 6.5, 7.5, and 8.5 km/hr. A generalized sensor-independent regression equation for AEE was derived. The regression equation was developed fur walking and running. The regression coefficients were predicted as functions of physical factors-age, gender, height, and weight with multivariable regression analysis. The generalized calorie estimation equation predicts AEE with correlation coefficient of 0.96 and the average accuracy of the accumulated calorie was $89.6{\pm}7.9%$.