MGLHMultivariate General Linear Hypothesis
MGLHMulticentric Giant Lymph Node Hyperplasia (cancer)
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This concept has a medialized CoR of the glenosphere and a lateralized humerus (MGLH); as a result, the humerus is positioned further lateral than the previous two designs.
Analyses were run using the multivariate general linear hypothesis (MGLH) module of Systat 5.0 (Wilkinson 1989).
We analyzed tracking data using the MGLH (Multivariate General Linear Hypothesis) procedure in SYSTAT (Wilkinson 1990), and considered P [less than] 0.05 the criterion for rejecting null hypotheses.
Mixed two-way plus block ANOVA was performed on each character using the Multivariate General Linear Hypothesis (MGLH) module of SYSTAT.
Analyses of variance were performed using the MGLH procedures in SYSTAT (Wilkinson et al.
- We used ANOVA (MGLH, SYSTAT 5.2.1) to test for density effects between low-density control and intraspecific competition treatments of each species.
Linear regression of fruit nitrogen concentration on nutrient treatment was estimated (MGLH module, SYSTAT 3.0) following the hypothesis TABULAR DATA OMITTED that tissue concentration would increase with soil nitrogen content.
All the above analyses were done using the Systat linear models (MGLH) module (Wilkinson 1990).
- Differences in components of reproduction among pollen recipients, treatments and pollination intensity were analyzed with a fixed model, 3-way ANOVA using the MGLH program in Systat (Wilkinson, 1988) for both (Mix) and (Het) pollinations.
For caterpillars that pupated successfully, the effect of the two host plant species on development time to pupation and pupal mass were compared by analysis of variance (SYSTAT procedure mglh, Wilkinson 1988).
The MGLH procedure of SYSTAT (1992) was used for the multiple regressions that are the foundation for the path analyses presented here.
In order to determine which of the hypothesized correlates (dispersal mode and gap preference) is most important in explaining seed size variation at the three sites, a series of general linear models (GLMs) was fitted to the data sets, using the multiple general linear hypothesis (MGLH) module in SYSTAT.