linear regression analysis


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linear regression analysis

a statistical method that aims to define the relationship between two variables, producing a value b, the regression coefficient. There are several assumptions that have to be made in carrying out the analysis, particularly
  1. that there is an independent variable x, e.g. time, which can be measured exactly, and also a dependent variable y, e.g. metabolic rate,
  2. that for every value of x there is a ‘true’ value of y . Linear regression analysis enables the fitting of a straight line to a scatter graph, using the equation y = a + bx, the a value being the point at which the regression line crosses the y -axis (the intercept).
References in periodicals archive ?
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).