"numerical interval" is correct and can be used in written English.
It is often used when talking about a range of numbers. For example, "The numerical interval of 10 to 20 represents a range of values."
This paper introduces the new concept of interval sensitivities applicable in the general context of numerical interval analysis.
In an actual implementation, a scaling procedure may be needed to keep the forward-backward variables within a manageable numerical interval.
After introduction of the concept, it is shown how interval sensitivities are calculated in the framework of general numerical interval analysis.
An alternative method, which we applied for this study, requires participants to make numerical interval estimates at a given level of accuracy (McKenzie et al. 2008; Soll and Klayman 2004; Teigen and Jørgensen 2005).
For subscale scores, the item-level responses were scored on a numerical interval scale ranging from 0 (no difficulty) to 100 (severe difficulty).
(ii) If the histograms of the A vectors (resp., P vectors) in Figures 3(a), 3(b), and 3(c) (resp., Figures 4(a), 4(b), and 4(c)) are sequentially analyzed, from group I to group III, then we can say that the numerical interval (mean − standard deviation, mean + standard deviation) for the A feature (resp., P feature) "moves" from left to right.
This is because the zero point of the Celsius scale is arbitrary and does not correspond to an absence of temperature.[3] Similarly, numerical intervals do not always carry empirical information.
In this opinion dynamics model, the agents express their opinions in numerical intervals, and the bounded confidences vary in a specified interval as time varies (i.e., dynamic bounded confidence).
However, other relationships could be defined, such as numerical intervals (e.g., "connection number" higher than 650), discrete ranges (e.g., "destination" in the IP address range 175.68.22.0/24), sets of values (e.g., "port" is 80 or 443), or complementary definitions (e.g., "user" is not "admin").
As in ROC analysis, the different ratings do not necessarily correspond to the same numerical intervals on the decision scale nor do all reviewers use the ratings with the same meaning since the ordinal scale is just used to test the probability distribution for each imaging technique [25].
We present SQUID, a method for system identification in which the space of potential models is defined by a semi-quantitative differential equation (SQDE): qualitative and monotonic function constraints as well as numerical intervals and functional envelopes bound the set of possible models.
