Prediction of body weight from testicular and morphological characteristics in indigenous Mengali sheep of Pakistan: Using factor analysis scores in multiple

linear regression analysis. Int.

Table 1:

Linear regression analysis of ISMI and quality of life.

Multiple

linear regression analysis indicated that LVEF, calcified plaque and LDL-C were independent risk factors of multi-vessel coronary artery lesion of old CHD patients (P<0.05), which is consistent with previous research findings.22

Table 1 reports multiple

linear regression analysis of composite FMS and individual FMS elements as a predictor of [PL.sub.Total], [PL.sub.MLTotal], [PL.sub.APTotal], and [PL.sub.VTotal].

Table 2 showed results of

linear regression analysis to predict quality of life from social acceptance.

Hierarchical

linear regression analysis among respondents with negative family-supportive organization perception (N = 96)-criterion: life satisfaction Model 4 B SE [beta] t 95% CI for B Constant 3.558 0.739 4.812*** 2.086-5.029 Marriage 0.014 0.010 0.171 1.420 -0.006-0.033 duration Number of -0.211 0.163 -0.159 -1.295 -0.536-0.113 children Commitment Parental -0.057 0.091 -0.081 -0.624 -0.238-0.124 role marital 0.060 0.104 0.075 0.573 -0.148-0.267 role occupational 0.320 0.124 0.282 2.573* 0.073-0.568 role Model 4--the last regression model in the analysis containing all study variables.

Multiple

linear regression analysis showed that BMI was the main independent predictor for systolic and diastolic blood pressure (DBP).

After the measurements,

linear regression analysis was done using cut perpendicularity as dependent variable and laser power P, cutting speed v and assist gas pressure p as independent variables.

After identifying various components of window systems that are believed to influence thermal performance of window systems, in this study, a multiple

linear regression analysis was conducted to investigate the extent of the effects of those components on Upvalue.

For Multiple

Linear Regression Analysis, the following constants were obtained:

IA maintained stable with age in both genders (

linear regression analysis: t = 0.56, P > 0.05 in male; t = 0.053, P > 0.05 in female).

Variables with the highest [r.sup.2] value in relation to actual weight were then used in our

linear regression analysis (P <0.05).