Therefore, we chose measure of progression (VSL), vigor (VCL), and swimming pattern (LIN) for statistical analyses.
The objective of the analysis reported here was to assess if there was consistency between the pattern of allele frequency differences between two cattle breeds and the pattern of statistical evidence for QTL identified in a cross-bred population of the same breeds.
The same pattern was seen for Statistical Subdivisions (p<0.001, by χ test), with proportionate immunity ranging from 7.5% to 42.9%, as illustrated in Figure 4.
Similar patterns of statistical inferences were demonstrated for symptom control for the Dominican Republic, that is, the lack of statistical significant differences between the mean symptom control between the chronically ill and the not chronically ill supports the validity of this measure.
For statistical pattern recognition, the following parameters were used: form factor, mean grey level, signal-to-noise ratio, mean gradient and correlation from the co-occurrence matrix.
The study addressed two objectives: to determine the hydraulic geometry, energy, and sediment mobility characteristics of neighbouring single and multiple channel river patterns, and to test for statistical differences in these characteristics between patterns.
The original contribution of the work is given by the use of an adaptation of Mel-Frequency Cepstral Coefficients as damage sensitive features, as their compactness and de-correlation characteristics make them particularly suited for statistical pattern recognition applications.
The pattern of the heatmap for statistical power was similar for these four traits and the heatmap for HDL-C is shown in Figure 1A.
To seek a statistical description of ensemble neural activity patterns from the recorded large datasets, we employed Multiple Discriminant Analysis (MDA) which has been shown to be an effective method for statistical pattern classifications of large neural data collected from well defined event categories [10], [12], [22], [23].
We employed Support Vector Machines (SVM) classification analysis [ 28], which has been shown to be a powerful tool for statistical pattern recognition.
