Examples of using Data variability in English and their translations into Portuguese
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In the sagittal plane,three components explained 90.7% of data variability.
Built for data variability- Likewise, it needs to handle multiple data types.
The first two dimensions of the multivariate analysis explained 72.8% of data variability.
The eingenvalues quantify the data variability explained to each dimension and range from zero to one.
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The first component retained 38% of the total data variability, while the second component had only 9.
That sample size will depend mainly on the value stipulated for the non-inferiority margin and data variability.
The Catuama Inlet presented a high biomass data variability, with higher values mainly during the spring tide.
For the quantitative variables, averages were used to summarize the information, andstandard deviations to indicate data variability.
Thus, an objective measurement of data variability can be obtained in each CEAP clinical class and for each parameter analyzed.
For choosing the number of components,it was considered that 90% of data variability should be explained.
Results: the data variability sample were compared using analysis of patterns of fixations in the region of interest, with the athletes in presenting compatible screening standard and those shown in studies involving the modalities studied.
Coordination of health care services with other levels of care showed higher data variability in health care services.
In multiple correspondence analyses Figure 1, the main plain dimension 1=50.1% anddimension 2=22.3% explained 72.4% of data variability.
In this sense, well conducted meta-analyses allow us to report that data variability potential compared to simple qualitative reporting in systematic reviews without meta-analysis.
Hence, the sample size was doubled and, consequently, data reliability was improved,maintaining the initial data variability.
The reduction of dimensionality occurs with the maintenance,as much as possible, of the data variability in several latent variables(components) that represent different possible syntheses of this variability18 18.
Using automated Gravimetric Sample Preparation and following the Good Weighing PracticeTM(GWP®)can help reduce data variability and OOS results.
Among the parameters usually employed, root-mean-squared difference RMSNN is particularly useful,as it expresses the amount of energy associated with data variability.
The results show that the axis of the first principal component of the interaction IPCA1 explained more than 90% of the data variability, justifying the choice for the AMMI1 model.
For the quantitative variables, means and medians were used to summarize the information, and standard deviations, minimum andmaximum values to indicate data variability.
Implementing automated gravimetric sample preparation andusing Good Weighing PracticeTM can significantly help to reduce data variability and OOS results.
For quantitative variables, means and medians were used to summarize the information, followed by minimum andmaximum standard deviations to indicate data variability.
Based on the results obtained from the descriptive analysis of the elderly population investigated, we can notice that the two groups formed males and females, as far as age is concerned, have normal distribution,with similar data variability, thus favoring statistical inference reliability.
The new variables, called principal components, are not correlated and ordered, so thatthe first component explains the largest proportion of data variability.
Student's t tests were used for quantitative variables, and Mann Whitney non-parametric tests wereused for independent samples, when data variability did not occur.
For quantitative variables, the following were used as measures: mean, median and standard deviation minimum andmaximum to represent data variability.
The variables that compose dimension 2 were more associated with the assistance providedby the health service. It was called"assistance service" and explains 7.2% of data variability.
As a result, it was possible to verify that the sigecap is easy to use,according to the obtained results, the perceived ease of use has led the largest percentage(37.33%) of data variability.
The variables that compose dimension 1 were more associated with the need toreach the health service. Thus, it was called"locomotion to the health service" and explains 7.3% of data variability.