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.