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The phrase "whose means are" is correct and usable in written English.
It can be used to refer to the resources or methods belonging to a person or group in a formal context. Example: "The organization, whose means are limited, is seeking additional funding to support its initiatives."
Exact(6)
"Noble ends do not save a speech-restricting statute whose means are poorly tailored," Justice Stevens said.
We define and as continuous random variables with negative exponential distributions whose means are given by and.
In the extremely simple case where features are uncorrelated and only two classes exist, it intuitively follows that the optimal set of features are those whose means are most different between the two classes.
In a one-way ANOVA, same sample sizes of 7 were obtained for all the four groups whose means are to be compared, assuming 100% tumour take.
We model read counts as independent Poisson random variables whose means are proportional to the bins' effective lengths and to the total abundances of isoforms associated with each bin.
Under certain assumptions, such as equal fitness for nonsynonymous substitution on the same locus, constant population sizes, random mating, no migration between species, and genic selection, the six counts in the extended McDonald-Kreitman table are independent Poisson random variables whose means are functions of the genetic parameters t d i v, θ s, θ r and γ (Amei and Sawyer 2010, 2012).
Similar(54)
Her own mother, whose means were just as modest, had a dedicated and affectionate aunt, and Clinton thinks that must have had an effect.
In a one-way ANOVA study, sample sizes of eight per group were obtained for the three groups whose means were to be compared.
A one way ANOVA analysis, sample sizes of 21, 23, and 22 were obtained from three groups whose means were to be compared.
As an evolutionary model, BayesFactors revealed that a "hyperprior" approach with exponential prior distributions, whose means were sampled from a uniform distribution between 0 and 10 gave the best fit.
It produces audio feature vectors whose mean is zero.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com