Still unsatisfied, he changes to the lowest magnification and goes so far as to uncenter
and blur the object, wondering if it might look better out of the corner of his eye.
COEFFICIENT AND RESIDUAL DIAGNOSTICS VARIABLE GROUPED ESTIMATION POOLED ESTIMATION VARIANCE INFLATION FACTORS Coefficient Uncentered
VIF Coefficient Uncentered
VIF Variance Variance LTCA_TA 1.017140 3.854787 0.272219 3.645721 LTCL_TA 0.262908 5.936670 0.128986 2.433991 LLEV 0.348001 6.082526 0.199794 2.035245 LSIZE 0.052157 1.367112 0.011406 1.094670 LASTAN 1.346973 3.744138 0.159287 3.178902 D2007 0.008350 1.184776 0.060271 1.145593 TEST OF NORMALITY OF RESIDUALS Jarque-Bera P.
Gender, disability status, reading IEP goals, and passage level (either middle or high school) were entered into the model uncentered
as they were dichotomous or nominal variables.
Table I reports uncentered
descriptive statistics and correlations for the variables.
If an uncentered
needle makes you antsy, just keep the DME where it should be or use an RMI, if available.
The interpretation of the marginal effects changes now compared to the case where data are uncentered
. Specifically, expression (3) measures the marginal impact of a one-unit change in inequality when the physical capital-human capital stock ratio is at its mean, while the respective coefficient using uncentered
data would measure the impact of a one-unit change in inequality when the above ratio is zero, which does not exist in the data.
When the main effects and interaction terms were uncentered
, there were no significant findings and VIFs ranged from 50 to 781, which indicates that multicollinearity was present.
In 1997, Kinnunen  first studied the Sobolev regularity of the usual centered Hardy-Littlewood maximal function M and showed that M is bounded on the first-order Sobolev spaces [W.sup.1,p]([R.sup.d]) for all 1 < p [less than or equal to] [infinity] (the same conclusion also holds for the uncentered
version M by a simple modification of Kinnunen's arguments or [2, Theorem 1]).
Cluster 3.0  and TreeView-1.1.6  with correlation (uncentered
) were used for unsupervised classification.
Considering the uncentered
[R.sup.2] would restrict comparability, since it is commonly much larger than the centered [R.sup.2] (see Wooldridge, 2008).
These vectors allow firms to be located in a multi-dimensional technological space where technological proximity between firms is determined as the uncentered
correlation coefficient between the corresponding technology vectors: