The use of multiple regression analysis (MRA) has been on the rise over the last few decades in part due to the realization that analysis of variance (ANOVA) statistics can be advantageously completed using MRA. Given the limitations of ANOVA strategies it is argued that MRA is the better analysis; however, in order to use ANOVA in MRA coding structures must be employed by the researcher which ...

Analysis of variance (ANOVA) is an approach used to identify differentially expressed genes in complex experimental designs. It is based on testing for the significance of the magnitude of effect of two or more treatments taking into account the variance within and between treatment classes. ANOVA is a highly flexible analytical approach that allows investigators to simultaneously assess the co...

The performance of face recognition algorithms is recently of increased interest. Empirical analyses of algorithms have traditionally been limited to rank-based scores such as cumulative match and receiver operating characteristics. This restriction to performance measures based on rank-based statistics arises because it is not possible to directly compare similarities output by algorithms. Thi...

The lattice-Boltzmann method is well suited for implementation in single-instruction multiple-data (SIMD) environments provided by general purpose graphics processing units (GPGPUs). This paper discusses the integration of these GPGPU programs with OpenMP to create lattice-Boltzmann applications for multiGPU clusters. In addition to the standard single-phase single-component lattice-Boltzmann m...

Multivariate analysis of variance and profile are often used for comparison testing in a multivariate perspective. In this paper, we treatment data psychological study. Forty patients were randomly assigned to one four therapies with exactly ten per therapy. Three standard instruments assess outcome. These the Symptom Index Kleptomania Evaluation (SIKE), Social Functioning Disorder Inventory (S...

Statistical methods used to test the null hypothesis are termed tests of significance. Selection of an appropriate test of significance is dependent on the type of data to be analyzed and the number of groups to be compared. Parametric tests of significance are based on the parameters, mean, standard deviation, and variance, and thus are used appropriately when interval or ratio data are analyz...

BACKGROUND Cognitive performance is a complex process influenced by multiple factors. Cognitive assessment in experimental animals is often based on longitudinal datasets analyzed using uni- and multi-variate analyses, that do not account for the temporal dimension of cognitive performance and also do not adequately quantify the relative contribution of individual factors onto the overall behav...