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effect of submerged biomass was held constant. The model was not 

 significantly correlated with submerged biomass (r = 0.431, p = 

 0.051, n = 22), however, when the effect of floating-leaved biomass 

 was held constant. This indicates that the above model predicting 

 floating-leaved biomass is not confounded by the variable 

 submerged biomass. The correlation coefficient between submerged 

 biomass and the model, however, failed to be significant at the a = 

 0.05 level of significance by a marginal amount, and the model may 

 prove to be confounded in certain applications. 



Submerged Biomass 



Stepwise multiple regression was performed on percentage data 

 for 20 diatom taxonomic groups (Appendix 3.10) to predict 

 submerged macrophyte biomass. Cp statistic values indicated that 

 the best model contained 11 diatom taxon variables and explained 

 91.6% of the variance in submerged biomass (p < 0.001, n = 22). The 

 adjusted R2 for the model was 0.824. This model, however, also 

 explained 86.9% of the variance in floating-leaved biomass (p = 

 0.004, n = 22) and seems to be confounded by that variable. The 

 partial correlation coefficient between the model and submerged 

 biomass was significant (r = 0.960, p < 0.001) when the effect of 

 floating-leaved biomass was held constant. The partial correlation 

 coefficient between the model and floating-leaved biomass (r = 

 0.938, p < 0.001) was also significant when the effect of submerged 

 biomass was held constant. The model to predict submerged 

 biomass, therefore, is not a useful predictive model because it is 

 significantly confounded by floating-leaved biomass. 



