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This way, variance that is not due to the explanatory variable of interest can be absorbed.
Each experiment was repeated for at least three times and results were expressed as mean ± standard deviation (SD) and one way variance analysis followed by Fisher's least significant difference (LSD) test was carried out using SPSS 11.0.
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The results were analyzed by ANOVA (one-way variance analysis).
Statistical significances of difference throughout this study were calculated using a Student t test and by one-way variance analysis.
One-way variance analysis (ANOVA) were applied to the height differences to test them whether they are significantly different or not.
We compared landscape metrics between years using a Kruskal Wallis one-way variance analysis with a significance level of 0.05 due to unequal variances and non-normal distributions (Dickins et al. 2013).
Statistical analyses were performed with a Kruskal-Wallis Test for one-way variance and with Tukey's Multiple Comparison Test.
Kruskal-Wallis one-way variance analysis was used to evaluate the differences between the groups.
Continuous variables were analysed with the ANOVA one-way variance test.
After the adjustment, new values of the groups were compared by one-way variance analyses.
Statistical analysis was performed by one-way variance analysis ANOVA using SPSS (version 17.0).
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