Sentence examples for multiple variables is from inspiring English sources
Making sense of big data sets with multiple variables is a classic challenge for the field of machine learning.
Optimizing for multiple variables is notoriously challenging; even when leaders unambiguously express their commitment to social and environmental goals by, for example, modifying their corporate mission statements, they face major tradeoffs.
The selection of optimal retort temperature profiles with a multistage ramp function involving multiple variables is complex and difficult to handle by conventional optimization methods.
Whilst it is well documented that monitoring multiple variables is most valuable in determining restoration success (Ruiz-Jaen and Aide 2005a, b; Cristescu et al. 2013), resource limitations make it essential to identify a concise suite of attributes that can be confidently used as measures to rapidly demonstrate restoration outcomes especially for large-scale projects (Menz et al. 2013).
Although the protocol omitted a discussion of corrections that we would have thought necessary, correction for multiple variables is designed to prevent false positives and there were no positives.
This result points to suppression, a situation in which the magnitude of the relationship between an independent variable and a dependent variable becomes larger when a third variable (or multiple variables) is included in the analysis.
The optimal properties of information-theoretic terms in assessing variability and correlation between multiple variables are familiar to the machine learning and statistics communities but perhaps not well known in fields of epidemiology and medical statistics.
Statistical evaluation of multiple variables was performed.
The credible intervals are increased as multiple variables are combined.
Multiple variables were tested and their impact on productivity metrics examined.
Rather than changing only one variable at a time, multiple variables are changed between experimental runs in order to isolate interaction effects between control parameters.
