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The importance of environmental and spatial variables in explaining species composition was determined by an RDA analysis using Monte Carlo permutation tests (999 unrestricted permutations) followed by forward selection to remove the non-significant variables from each of the explanatory sets.
Small models with each of the explanatory variables were fitted to data.
Plots of the deviance residuals versus fitted values and against each of the explanatory variables were used for model validation.
Below, we test for composition changes between pre-reform and post-reform periods using each of the explanatory variables one by one as the dependent variable.
The results as presented in Table1 indicate that there is a significant relationship between each of the explanatory variables and ANC use except age and employment status.
Both the ANOVA and Kruskal-Wallis analysis have been performed to measure and test the statistical significance of differences in incident duration for each of the explanatory variables.
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However, only one observation for each level of the explanatory variable was measured including one community close to a swine operation, one community close to two cattle operations, and one control community.
Probabilities π ˆ were fitted for each category of the explanatory variable.
Data were analyzed by means of a logistic regression [ 53] using each one of the explanatory variables above.
Table 1 shows the number and percentage of individuals who used HS, separated by sex and for the total, for each level of the explanatory variables.
In logistic regression, the dependent variable is a formulation of counts of the two alternative outcomes at each level of the explanatory variable.
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