Exact(10)
The physical causes of each dimensional change are summarized.
The data were analyzed using stepwise regression models in which neuropsychological scores were in entered to predict each dimensional score.
Specifically, the first set (location 1) has a Gaussian-like roundness to each dimensional distribution, the second set (location 2) has a reasonably pointed peak with smoothly sloping sides, quite triangular in appearance, and the third set (location 3) has a marked kink in the sides and is very heavy tailed, again with each dimension showing basically the same shape.
Then, we apply a unique binary basis number generation algorithm, which maps test data into multi-dimensional space and generate coordinate range identifier (a unique number used to identify intercepts in the multi-dimensional space) of each dimensional to a binary vector at once.
Each dimensional module encodes the presence of feature contrast for all locations across the visual field, with feature contrast computations being modulated by the spatial separation between neighboring items [4].
Thus, the reliability coefficients (α values) for each dimensional scale exceeded the minimum of 0.7 suggested by Nunnally [ 77].
Similar(50)
We endow each finite dimensional real vector space with the Euclidean topology and each infinite dimensional Cartesian product of finite dimensional real vector spaces with the product topology.
Consequently, for each finite dimensional subspace Y 0 ⊂ Y, the condition ( Φ 2 ) holds.
Consequently, for each finite dimensional subspace (Y_{0}subset Y), the condition ((Phi_{2})) holds.
For each finite dimensional subspace E ˜ ⊂ E, by (4.30), we know that φ ( u ) → − ∞ as u ∈ E ˜ and ∥ u ∥ → ∞.
(A1 there exist constants such that, (A2)for each finite dimensional subspace, there is such that for all with.
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