Stepwise multiple regression analysis can identify the significance of the

partial regression coefficient and gradually remove nonsignificant morphological traits; this method was used to construct multiple regression equations between total body weight and morphological traits.

On GEE analysis, TBSA, surgery, and outcome affected LOS, and the

partial regression coefficient for TBSA was 5.26, surgery 33.75, and outcome 2.08 [Table 1].{Table 1}

B1=0.5, is a

partial regression coefficient of land size and tell us the influence of land size on the maize production ceteris paribus.

In multiple coefficients, each

partial regression coefficient represents the importance of each sensor.

The inter-individual variation in behaviour, scored as behavioural similarity between each pair of individuals, is non-independent, so Whitehead addresses the significance of the

partial regression coefficient by bootstrapping a distribution of

partial regression coefficients, where the similarity scores of the independent variables are randomized across individuals.

Multiple linear regression modelling adjusts each regression coefficient for the effects of correlations with the other independent variables to produce a

partial regression coefficient. The partial coefficient estimates the slope of the linear relation between the dependent variable and the uncorrelated part of the independent variable; consequently, the value of a partial coefficient usually differs from that of the simple linear regression relation between 2 variables.

The value in parenthesis underneath each variable is the F-test of the null hypothesis that the variable's

partial regression coefficient is zero against the two-sided alternative that it is not zero.

This specification means that the estimated

partial regression coefficient on the income variable is an estimate of the income elasticity of the demand for lottery products.

In OLS regression the partial slope

partial regression coefficient indicates the change in the expected value of the dependent variable for a one-unit change in a given predictor, while the rest of the predictor variables are held constant.

The best example is multiple regression analysis in which the weight for each of these independent variables known as a

partial regression coefficient is computed.