The phrase "amount of relevant variance" is correct and usable in written English.
It can be used in contexts such as statistics, research, or data analysis when discussing the extent of variance that is pertinent to a particular study or analysis.
Example: "The amount of relevant variance in the data indicates how much the variables are influencing the outcome."
Alternatives: "degree of pertinent variance" or "extent of applicable variance".
In particular, the PCA-Gabor method captured the largest amount of experimentally relevant variance, while using the least amount of overall data variance.
Partial eta-squared values for the three methods are summarized in Table 2. Here, for sex, RT, and difficulty, the nominal order of the amount of experimentally relevant variance in the statistical effects was the same, largest in the PCA-Gabor, next in the PCA, and the least in the SMP-Gabor.
In the course of "Serial," we learned a huge amount of relevant information that proved inconclusive.
This is analogous to the results of the simulated data with noise, where the PCA-Gabor method extracted the most signal power in terms of experimentally relevant variance, while excluding the largest amount of noise power in terms of experimentally irrelevant variance.
Qualitative research techniques add vast amounts of relevant data [ 26].
Finally, these results corroborate the observation that intelligence generally does not predict the exertion of creative activities (although latent factors of intelligence and ideational originality displayed a large amount of shared variance) but is particularly relevant to creative achievement (e.g. Batey et al., 2010).
Most methods for evaluating reliability of clinical performance assessments have in common that they estimate the amount of variance in student ratings considered relevant in relation to the amount of variance due to source(s) of 'noise' or error (Turnbull et al. 1998; Downing 2004).
The effect is large enough to explain substantial amount of the variance in party divergence across states.
Data generated in postharvest research are characterised by an inherent large amount of biological variance.
The first PC accounts for a maximal amount of total variance, while the amount of variance that was not accounted for, is accounted by the second PC.
