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In tests in such environments, sample sizes are often smaller than 100, violating typical assumptions of many standard statistical methods.
Many standard statistical models used to examine population dynamics ignore significant sources of stochasticity.
Unlike many standard statistical distributions, GLDs are characterised by inverse quantile functions.
Formulas for computing sampling errors applied in many standard statistical software packages such as the base module of SPSS assume simple random sampling.
The time dependent method can be applied in many standard statistical software packages.
This implies that the independence assumption which many standard statistical models are based on is violated.
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Many standard and advanced statistical methods are commonly used for this purpose, such as linear/nonlinear regressions, Bayesian inference, machine learning, data mining, and so on.
This chapter focuses on two of these heuristics, "Take The Best" and Minimalist, and compare their performance with that of standard statistical strategies that weigh and combine many reasons, such as multiple regression.
Such approaches typically reflect the massive nature of many health related databases and clinical data repositories where standard statistical methods may not easily apply.
The biological impacts of habitat fragmentation are routinely assessed using standard statistical modelling techniques that are used across many ecological disciplines.
All Facility Core members are facile in the R programming language, in standard statistical software packages such as SAS, SPSS and STATA, and in many specialized packages and applications.
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