Exact(16)
A Wald-type statistic is then used to test independence.
Formal tests like the Durbin-Watson test (independence), Breusch-Pagan test (constant variance), and Shapiro-Wilk test (normality) can check the model assumptions [33].
Owing to the sample size, no multivariate regression could be performed to test independence between mHLA-DR and cytokines for the prediction of risk of secondary acquired infection occurrence.
Assumptions of linear regression were tested with Shapiro Wilk test (normality), partial residuals plots (linearity), Durbin Watson test (independence of errors), non-constant error variance (homoscedasticity), and variance inflation factor test (multicollinearity).
To test independence with a sample of size n, Spearman's ρ is compared to a centered Normal with variance 1/(n−1) and Kendall's τ to a centered Normal with variance (4n+10)/{9n(n−1)} (see e.g. Capéraà and Van Cutsem1988).
The number of samples considered is representative of the statistical calculation, because the conditions of the ANOVA test (independence of the samples, normality of the data points among the population, absence of outliers in the population, and almost equality of population variances) hold.
Similar(44)
Pandit [11] investigated the LMPRT for testing independence against a weighted contamination alternative.
We explore correntropy from a statistical perspective and discuss its properties in the context of testing independence.
Remark 2 mentions that there is no asymptotic loss of power in testing independence using a scalar-weighted version of Spearman's ρ.
We therefore tested independence in two ways.
Assumptions for parametric testing (independence, normality, equal variance) were met for all measured response variables.
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