linear regression, logistic regression, and analysis of variance) do not properly account for the nested structure of such data, and can yield biased parameter estimates, and incorrect standard errors
(Bryk & Raudenbush, 2001).
The mean score for this statement is 3.58, standard error
0.14 and standard deviation is 1.447.
The standard errors
of disability estimates were scattered over a wide range for the incomplete data when compared with the original data.
In Figure 3 linear relationship with variance 1x10-4 and standard error
is about 0.01.
This data set resulted in a model that provided a standard error
of only 4.3 [cm.sup.3]/100 g, as shown in figure 6.
Estimates of the standard error
are 0.149 under SRS, 0.167 under GVF, and 0.168 when replicate weights are used.
Two alternative approaches were taken to estimating the standard errors
of the coefficients in the specified model.
Panel D of the figure shows the standard error
of the estimated components, where the standard error
is computed by estimating the standard deviation of the first term on the right-hand side of (1.6) using the estimated parameters of the AR model.
2) corresponds to a rational function regression (1), with a standard error
of S=11.887 and a correlation of r=0.9966:
In replicated yield trials, without imazamox applications in Idaho, Oregon, and Washington in 2001 to 2004 (total of 85 environments) Idaho 587 had a grain yield of 6.7 Mg [ha.sup.-1], compared with 6.7 Mg [ha.sup.-1] for Stephens, 6.6 Mg [ha.sup.-1] for Brundage 96, and 6.6 Mg [ha.sup.-1] for Madsen (standard error
0.3 Mg [ha.sup.-1]).
The standard error
of the mean for the pretest social responsibility data was SE = .0658.
The value of 'twice the standard error
of the measure of the Australian Statistician's estimate of the net undercount for that Territory at the last Census' (8) is obtained.