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However, the statistical models used in the study are robust to deviations from the normal distribution (Boomsma and Hoogland 2001).
Secondly, PLS is a component based approach, and as such, it is relatively robust to deviations from a multivariate distribution not requiring a large sample size [12].
The method used here is actually robust to deviations of the data from the simple Gaussian model [23, 24], so even in such cases, it provides accurate, reliable results at a much lower computational cost.
This statistic is robust to deviations from normality of relDAS28.
Therefore, the present investigation applied statistics that are robust to deviations from normality, symmetry, and heteroscedasticity.
Sensitivity analyses are necessary to determine whether the results are robust to deviations from this assumption.
Similar(40)
However, because of some non-normality of the data (over-representation of zero pairing success), we (b) also performed an analysis more robust to deviation from normality using a logistic regression with an ordered cumulative logit model with the same trait variables.
ANOVA is robust to deviation from this assumption where sample size is large [ 56].
Although many of these laboratory values are slightly skewed, we decided to analyse the data without transformation the large sample size means the statistical analyses should be robust to deviation from normality and transformation can make results difficult to interpret.
Fisher's LDA is easier to compute, while the DF approach is more robust to deviation from normal distribution, but is computationally prohibitive if more than two markers are involved [ 19].
Hence, we do not restrict deviations by an insurer to be robust to further deviations by other insurers.11 Also, unlike Grossman's notion, we do not allow insurers to withdraw contracts that were previously offered.12.12
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