The phrase "a plausible value" is correct and usable in written English.
It can be used when discussing a value that is reasonable or believable within a certain context, often in academic or analytical writing.
Example: "In our study, we found a plausible value for the average temperature increase over the last century."
Alternatives: "a reasonable estimate" or "a credible figure".
The vectors provide a plausible value for each of the PISA 2006 reporting scales.
When computing a plausible value, a mathematical distribution around a reported value is first calculated and then each observation is assigned a set of random values drawn from this distribution (OECD, 2009b).
When the Victorian pandemic was simulated to reflect a plausible value of R = 1.4 and a serial interval of 2.8 days, earlier undetected cases needed to be invoked to reflect the observed epidemic pattern.
We explored the effect of fixing the time delay and found that fixing it at 80 ms, a plausible value given neural time delays in the feedback loop, gave only a modest 4% increase in the error (from 7.43 to 7.72%).
To obtain a plausible value for ψ max, we estimated ψ s in (19) pretending either sex or age was the unobserved covariate x.
Thus to obtain a plausible value for ψ max, we suggest estimating ψ s, as defined in (19), based on observed covariates.
Here the tuning parameters are either optimized through a correct inner cross-validation procedure or fixed to a single plausible value in order to handle all 10 classifiers equally.
For example, the BIC suggests an optimal number of clusters K around 10, while the AIC gives a less plausible value of K > 20.
Decision theory was used to calculate the optimum treatment of microscopic squamous cervical cancer using probabilities obtained from an exhaustive literature review and a range of plausible value estimates.
We assumed a unique (and plausible) value for the overall residual variance in order to allow for a direct comparison and interpretations of the results.
Essentially, MI is a means for representing uncertainty in missing data, by producing a distribution of plausible values for a missing variable in a record, given the values of that record's non-missing covariates.
