linear regression


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Related to linear regression: Multiple linear regression
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linear regressionlinear reg...
  • noun

Synonyms for linear regression

nounthe relation between variables when the regression equation is linear: e

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References in periodicals archive ?
In agreement with the current study, many authors also reported that use of factor scores analysis in multiple linear regression modeling removed multicollinearity problem (Keskin et al., 2017a, b; Cankaya et al., 2009; Yakubu, 2009; Eyduran et al., 2009, 2010; Jahan et al., 2013; Khan et al., 2014; Beyhan et al., 2016).
In order to improve the accuracy of prediction of observed BW from linear body measurement, multiple linear regression equations were developed.
The coefficient of determination expresses the percentage of the PAPm's variation, explained by each variable separately and express their strength of prediction (prediction's potential).We calculate the coefficients of determination R2 for the linear regression and non-linear regression models.
The linear regression statistics for the 4 subclass comparisons are shown in Table 2.
(6) Establish the linear regression model and the disease identification system.
This implies that per capita income variable as a whole can be explained by the variable of GRDP at CMV, GRDP at CP and Total Population/life.The result of the analysis of multiple linear regression model of table 1 is obtained by the following equation:
Subjects were grouped according to sex, and analyzed using linear regression analysis, deriving the inferred function of male age: Y=64.333-468.811 (PV/TV), R=0.435; the inferred function of female age: Y=76.445-843.186 (PV/ TV), R=0.691.
The next step of the study consisted of correlation and simple linear regression analysis of the data in order to identify the relationship between the dependent variable Y (physico-chemical characteristics of suspended particulate matter) and the independent variables X (physico-chemical characteristics of suspended particulate matter) [30].
The variables selected via Stepwise were used to compose the multiple linear regression equation in the simulation of oat grain yield.
The regression sum of deviation squares [SS.sub.R], defining a part of the distribution of Y estimates around the average [bar.Y] and explained by linear regression Y in respect of variables [X.sub.j], i.e.
Obviously, the fuzzy linear regression is essentially an optimization problem, which is solved by minimizing the objective function subject to the following constraints:
Because factors that affect sucrose content (Suc) include polarization (Pol) and brix (Bx), multiple linear regression analysis is performed as follows:
The relationship between the parameters, with a positive correlation between the F-PEF, S-PEF, and the FEV1, has been studied in simple linear regression. The parameters that have a significant correlation in simple linear regression were analyzed in multiple linear regression to retain the influential parameters for F-PEF, S-PEF, and FEV1 in a statistically significant way.
Linear regression is the most widely used statistical model in data analysis.