Sentence examples for many statistical procedures from inspiring English sources
Many statistical procedures assume that the variables are normally distributed.
On the other hand, it has been recognized in many statistical procedures that individuals vary in their vulnerability to causes of death or disease, response to treatment, and impact of various risk factors.
It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures.
He showed that the OD curve is a complete representation of the rank-order properties of data and many statistical procedures follow naturally from assessment of the curve.
There are many statistical procedures for these purposes, and they differ in their assumptions and objectives and in the way they protect against type I and type II errors.
It provides the framework for many statistical procedures intended to estimate causal effects and demonstrates the limitations of observational data [ 10]. 2. Counterfactual causality has also stimulated the invention of new statistical methods such as g-estimation.
However, intensive data collection based on intensively observed smaller sample sizes is emerging in many areas of behavioral and health research in aging and beyond and many innovative statistical procedures have been suggested to optimally treat such a data situation [ 74, 75].
However, it allows for more subjectivity in the decision-making process than many other statistical procedures, which can be considered a problem (Roberson et al. 2014).
This scenario reflects, at least approximately, many of the statistical procedures found in medical journals.
Their method is thus distinguished from many of the classic statistical procedures for understanding genetic subdivision, such as F-statistics [15] and analysis of molecular variance [16].
The software and codes necessary to carry out the kriging procedure are widely available (in many statistical packages, and/or the principal investigator) and does not require a profound understanding of imputational statistics.
