Search results

The parametric [[variance]] of the total is

For the variance calculation with the Horvitz-Thompson estimator we also need to know the j The parametric [[error variance]] of the total is

with estimated variance

The parametric [[error variance]] of volume estimation from a sample of size ''n'' is ...y simulation, as a single sample of n = 1 does not allow estimating error variance. A linear probability density function (defined by tree height and the def

... (<math>dm^3</math> as Y-axis and ''dbh'' (cm) as X-axis showing unequal variance across ''dbh'' classes (Kleinn 2007<ref name="kleinn2007">Kleinn, C. However, for the unequal variance case of volume functions, the confidence interval has the shape of a trumpe

...700 and 800, respectively. From these, mean number of stems per ha and its variance can be calculated. |with variance per plot

...ates, usually quantified in terms of [[root mean square error]] or [[error variance]] or [[standard error]]. It describes inherent residual variability and can ...ent strata in [[stratified sampling]] under consideration of the estimated variance of the target variable in different strata.

... that this approach performed consistently best in terms of bias and error variance when compared to other empirical estimators.

...noise” to the mean value we reduce or eliminate the downward bias of the variance estimator. ...gression is a sort of “moving average” this will again likely bias the variance downwards.<br>Similarly to the previous approach, we can add a residual ran

...oth cases; but this is not the case for the per-plot parametric mean and variance (Kleinn and Vilcko 2005<ref name="kleinn_vilcko2005">Kleinn C. und F. Vi ...the stand boundary|border plots]] with smaller size. The per-plot mean and variance will be different between the two sub-divisions; this is certainly an undes

... an approximation of the parametric variance <math>\sigma_p^2</math>. The variance of the estimated mean <math>\bar y</math> for a random sample of a given si ...cted value and the true parametric value ([[bias]]) and, consequently, the variance of the estimated means characterizes their variability around the biased ex

...ots – and it is the variance between the plots that determines the error variance.

...rger population but with exactly the same characteristics like mean and variance), then the 10% sample produces considerably much more precise results! ( ...nsity keeps constant. The population characteristics (in terms of mean and variance) were exactly the same because all data sets were generated from the same i

|parametric variance |estimated variance

... defines the [[Confidence interval]]; it is the square root of the [[error variance]]. Error variance and standard error can be estimated for any estimated statistic; here, we r

...d” by the linear operator “minus”. Then, the expected value and the variance of this linear combination can be determined along the rules presented in ...change is estimated then along <math>c=\bar{y_2}-\bar{y_1}</math>, and the variance of the estimated change is the sum of the variances:

...population (in our case: the forest cover), then the parametric population variance is where <math>q=(1-p)</math>. The variance, obviously, is a function of the mean ''p'' only!

and the variance of the estimated total forest edge length is estimated by ...tors for total forest edge length in km <math>\hat{L}_{tot}</math> and its variance <math>\hat{var}(\hat{L}_{tot})</math> are

! Bole volume variance (<math>m^3/h)^2</math>

...intra-cluster correlation coefficient can be incorporated. Then, the error variance of the estimated mean is ...o, the parametric intra-cluster correlation coefficient and the parametric variance in the population occur in this formula.