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.
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In the indication of potential variables for inclusion in the multiple linear regression models, the mean square significance of the variables analysed by the StepWise technique is presented (Table 3).
The dependence of the gross output generated by a certain wind farm and its coefficients upon wind speed and air density based on multiple linear regression analysis model and (14) obtained should be calculated.
The problem of estimating the parameters of a multiple linear regression under assumption of generalized Gauss-Laplace distribution of the error is a hard problem which can be solved only numerically and it involves an optimization problem with m + 3 constrains, where m is the number of unknown (to be determined) coefficients of the multiple linear regression.
The data regarding population of Jassid (Amrasca bigutulla bigutulla (Ishida) were also processed into simple correlation and multiple linear regression analysis of variance along with coefficient of determination values with the weather factors for 2009 and 2010 individually and on cumulative basis.
Multiple linear regression analyses showed that only negative thinking (p < .
Multiple linear regression of PTH peptides measured by LC-MS/MS vs PTH measured by 2 different immunoassays.
The zinc (Zn) concentrations were determined by stepwise multiple linear regression analysis of first degree derivation of spectral reflectance peaks with maximum R2; possible wavelengths for zinc (Zn) measurements were determined.
Multiple linear regression model was fitted to describe the relationship between yarn count and five input parameters.
Table 1 below shows the multiple linear regression model summary and overall fit statistics.
It explains basic statistical concepts, the principles of statistical modeling, the linear regression model, the different types of covariates that may arise and their preliminary screening using graphical techniques, multiple linear regression, logistic regression, Poisson regression analysis, time-to-event regression, building a parsimonious regression model, studies that involve repeated measures, the use of regression trees to identify homogenous subgroups, and model structure, multi-level models, and fractional polynomials.
A Forward: LR method of multiple linear regression models was used to determine the predictors of WC.

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