Sentence examples for maximizing variability from inspiring English sources

Unless one is interested in the average exposure of the population as a whole (in which case maximizing variability in hour of sampling might be a good option for quickly metabolized compounds), this variability is a potential source of nuisance.

To maximize variability in affective reactivity, depressed, recovered depressed, and healthy control participants (n = 33) were classified as high or low in PC based on a median split.

U.S. Census block groups in Seattle-King County, Washington and BaltiMaryland-Washingtonington DC regions were selected via geographic information systems to maximize variability in walkability and income.

The sampling design was not hierarchical, but instead was designed to maximize variability between observations in both disease prevalence and the environmental predictors.

The primary sampling frame of schools was typically stratified by indicators of socio-economic status in order to maximize variability within sites.

Namely, one could maximize variability of endotoxin across subjects, thereby increasing σ b, or sample endotoxin repeatedly to better estimate true room-specific mean levels within homes.

To maximize variability in the selected neighborhoods only meshblocks in the highest and lowest tertiles for walkability scores were eligible for selection.

This statistical technique started with k random (or selecting the most different k cases) clusters, and then moved objects between those clusters with the goal to (1) minimize variability within clusters and (2) maximize variability between clusters.

The algorithm starts with k random clusters, and then move objects between those clusters with the goal to 1) minimize variability within clusters and 2) maximize variability between clusters [ 21].

Each country and geographical jurisdiction has its own limited variability in the prevalence of obesity and each of the potentially modifying factors at different levels, yet an international study is able to maximize variability in these factors.

The k-means procedure identifies relatively homogenous subgroups while maximizing the variability between clusters and is able to handle larger amounts of classification variables.