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This problem arises because many statistical analysis techniques applicable to surveillance, such as Generalised Linear Mixed Models [ 4] (GLMM), depend on the assumption that observations are statistically independent.
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Many supervised statistical analysis methods have been described for analyzing microarray data [ 10].
Among the many statistical methods, significance analysis of microarray (SAM) [ 16, 17], empirical Bayes analysis of microarrays (EBAM) [ 18], and empirical Bayes statistics (eBayes) [ 19] are three commonly employed approaches to screen DEGs.
There remain many statistical challenges in analysis of functional connectivity and network architecture from BOLD fMRI multivariate time series.
Functional data analysis encompasses many methodologies for statistical analysis of data coming from measurements concerning continuous phenomena, e.g., curves or surfaces [ 47].
To create time series of sufficient length for statistical analysis, many subsequent stress biomarker measurements are needed for each participant.
In this paper, I examine William Whewell's (1794 1866) 'Discoverer's Induction', and argue that it supplies a strikingly accurate characterization of the logic behind many statistical methods, exploratory data analysis (EDA) in particular.
There's a good collection available including: –Web sites like Many Eyes or Gooddata; –Statistical analysis and visualization tools including "R" that can churn out incredibly sophisticated charts; –Databases such as MySQL or PostgreSQL will help you build a data warehouse; –Charting packages that are part of almost every major scripting languages.
Many also include a statistical analysis plan.
Many methods of multivariate statistical analysis rely on a correlation matrix as the initial data, e.g. principal component analysis, factor analysis or path analysis.
In many scientific areas where statistical analysis is performed, the problem of dealing with explanatory variables subject to measurement error is present.
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