Here's what the parts of the formula for calculating variance mean: s2 = Variance.
Comparing expected and observed selectivity across the complete set of drug combinations (example comparisons in Fig. 3, right column), we found that 41 of 66 combinations showed a significant difference in selectivity from additive expectation based on self self experimental variance (mean = 0.12, std. dev. = 0.10) (Supplementary Fig. 3).
Several landscape metrics were chosen to describe the landscape pattern (spatial share, mean patch size, patch size coefficient of variance, mean shape index, mean nearest neighbor distance, mean proximity index, and interspersion and juxtaposition index).
Therefore, the Dresher variance mean V r, s ( f, p ) has a wide mathematical background.
Remark 2 We remark that the Dresher variance mean V r, s ( x, p ) extends the variance mean V r, 2 ( x, p ) (see [2], p.664, and [13, 21]), and Lemma 1 is a generalization of (2.23) of [19].
In particular, we establish a Dresher variance mean inequality min t ∈ Ω { f ( t ) } ≤ V r, s ( f, p ) ≤ max t ∈ Ω { f ( t ) }, that is to say, the Dresher variance mean V r, s ( f, p ) is a true mean of f.
The negative binomial was used to describe over-dispersed (zero-inflated) count data (variances > mean), rather than Poisson distribution data (variance = mean).
Variance-mean analysis has shown that each simple synapse has 2 8 DSs31.
Based on our previous analysis of variance-mean data31, we assumed in our simulations a fixed number of 4 DSs per AZ (Fig. 1e).
As in our previous report22, we obtained δ by minimizing the sum of least square deviations of variance-mean results during trains.
As shown in Supplementary Fig. 10, it also accounts for variance-mean results, both for asynchronous release for each stimulation and for cumulative release.
