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Correlation between two variances was estimated using linear regression analysis with a Pearson test.
Statistical differences between the two groups were evaluated by analysis with t-test; analysis of linear correlation was used to evaluate the correlation between two variances; q-test of analysis of variance (ANOVA) was used to analyze multiple comparison.
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The figure shows that when N is large and n small, there is practically no difference between the two variances.
Even when population size N is 40,000, and when sample size n is relatively large, the difference between the two variances can be relevant.
Next an analytical expression for the variance of the inventory position is derived and used together with the bullwhip expression to determine suitable ordering system designs that minimise both bullwhip and inventory variance for a range of weightings between the two variances.
We have previously derived an inequality Vip>Vg between the two variances, by assuming evolutionary stability of the population distribution P x, a), that is preservation of single-peakedness through the course of evolution [18] (see Supporting Text S1).
The difference between these two variances gave an estimate of −0.05 for the gametic phase disequilibrium covariance.
Still, the proportionality between the two variances V ip (i) and V g (i), albeit not so sharp, holds, as already observed in the region with larger V g (i) in [ 1].
The variable d' reflects the distance between two equal-variance Gaussian strength distributions, ci reflects the response criterion at point i, and Φ is the cumulative normal response function.
For bias estimates, the optimal criterion is centred at zero between two equal-variance Gaussian distributions representing signal and noise distributions.
This design typology is characterized by two variance components: between acquisitions and between subjects.
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