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The principal component analysis (PCA) compresses and classifies data by evaluating a new set of variables, smaller than the original set of variables, which holds most information of the dataset.
This persistent inequality is in part because the conditions for a successful campaign are clustered around a small number of variables: smaller, urban, wealthier, and racially/ethnically diverse districts favor women candidates.
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The variable size of gap is a fuzzy variable, and is divided into three linguistic variables: small gap, medium gap, and large gap.
In addition to on-line measurement of state variables, small samples will need to be withdrawn and analysed during operation to follow changes in state variables, including catalyst characteristics.
For categorical variables, small units (≤5%) were merged into adjacent subgroups.
Crucially, order did not interact with any of the other variables (smallest p =.214).
Interaction terms were not statistically significant except for the variables small for gestational age and preterm delivery (see Supplemental Material, Table S1).
With the given 63 complete data sets which were included in the analyses and 3 independent variables, small effects could probably not have been detected.
Sample size of 958 eyes has power of the test larger than 85% in multivariate analysis with seven independent variables, small effect size, f = 0.02 (R = 1.96%) and level of significance 0.025 [ 16].
However, even within the normal range of clinical variables, small variations in lipoproteins [ 48, 49] and blood pressure [ 50] are associated with varied outcomes and risks for cardiovascular disease and death.
The particle growth follows the general growth power-law during Ostwald ripening, but the growth exponent is found to be variable, smaller in the earlier stage and larger in the later stage.
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