multiple linear regression


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multiple linear regression

[¦məl·tə·pəl ¦lin·ē·ər ri′gresh·ən]
(statistics)
A technique for determining the linear relationship between one dependent variable and two or more independent variables.
References in periodicals archive ?
Factor analysis scores in a multiple linear regression model for the prediction of carcass weight in Akkeci kids.
To affirm the usefulness, first, a multiple linear regression model which describes the relationship of SM excitation current with regard to four predictor variables such as load current, power factor, error and excitation current changing is considered, and then GASA technique is applied to optimize five regression coefficients in such regression model.
These score values were used as independent variables in the stepwise multiple linear regression analysis to determine the most significant PCS for water demand prediction.
We applied the proposed descriptors by multiple linear regressions for 30 molecules in the whole formation and we used the correlation coefficient (R) and the mean squared error (MSE) to select the best performance of regression.
We used SigmaStat (Version 3.5) for all univariate analyses and stepwise forward and multiple linear regressions. We tested for interspecific differences in the use of substrate type and microhabitat (nominal variables) with a Chi-square ([chi square]) test.
(i) Multiple Linear Regression Analysis with 2 inputs (MLRA2): A =-1, 6229; B = 2, 7449; C = -42,2721
Put the 11 features into multiple linear regression models.
Separate multiple linear regression analyses were performed to analyze each independent variable potentially affecting CT.
This study aimed to validate the use of rainfall, thermal time and N as potential variables to compose the multiple linear regression model and simulate wheat biomass yield for silage production under N supply conditions during the cycle, in the systems of succession.
Multiple linear regression and descriptive regression analysis method, regression model of Gross Regional Domestic Product (GRDP) variable at Current Market Value(CMV) and Gross Regional Domestic Product at Constant Prices (CP), and total population have negative and significant impact to per capita income.
Criteria for selection of the best multiple linear regression model were the statistics: correlation coefficient (R), squared multiple correlation coefficient (R2), adjusted correlation coefficient ( ), Fisher ratio (F), root mean square error (RMSE), Durbin-Watson value (DW) and significant (Sig).
In order to predict the stated spirometric measures using age, height and weight, simple and multiple linear regression models were used.

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