Traditional analysis of variance (ANOVA) tests are based on the assumption of homogeneous error variances, which often fails in real situations. Violation of this assumption affects not only the power of the standard F-test, but also its size. When a design is unbalanced, the effect of unequal error variances is even more complicated. In this paper, we study the effect of heterogeneous error va...

Characterized by simultaneous measurement of the effects of experimental factors and their interactions, the economic and efficient factorial design is well accepted in microarray studies. To date, the only statistical method for analyzing microarray data obtained using factorial design has been the analysis of variance (ANOVA) model which is a gene by gene approach and relies on multiple assum...

Analysis of variance (ANOVA) refers to statistical models and associated procedures, in which the observed variance is partitioned into components due to different explanatory variables. It provides a statistical test concerning if the means of these several groups are all equal. In its simplest form, ANOVA is equivalent to Student's t-test when only two groups are invloved. The analysis of var...

Brute force matching of binary image feature descriptors is conventionally performed using the Hamming distance. This paper assesses the use of alternative metrics in order to see whether they can produce feature correspondences that yield more accurate homography matrices. Two statistical tests, namely ANOVA (Analysis of Variance) and McNemar’s test were employed for evaluation. Results show t...

This paper focuses on analyzing extensive results generated from running diverse multi-robot patrolling algorithms with different configurations towards measuring the influence of the variables of the general problem. In order to do this, a statistical technique by the name of Analysis of Variance (ANOVA) is employed to compare the parameters and identify the ones which give raise to the total ...

Gene selection is an important issue in analyzing multiclass microarray data. Among many proposed selection methods, the traditional ANOVA F test statistic has been employed to identify informative genes for both class prediction (classification) and discovery problems. However, the F test statistic assumes an equal variance. This assumption may not be realistic for gene expression data. This p...