Zusammenfassung der Ressource
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PCA stands for [blank_start]________[blank_end] [blank_start]________[blank_end] [blank_start]________[blank_end].
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Principle
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Principal
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Parametric
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Post-hoc
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Component
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Components
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Correlate
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Correlates
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Criterion
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Criteria
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Analysis
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Analyses
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Asshole
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Arsehole
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Array
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Arrays
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CCA stands for [blank_start]________[blank_end] [blank_start]________[blank_end] [blank_start]________[blank_end].
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Canonical
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Correlate
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Correlation
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Correlates
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Correlations
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Component
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Components
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Canonical
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Component
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Components
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Correlate
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Correlation
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Correlates
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Correlations
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Criterion
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Criteria
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Analyses
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Analysis
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The term representing the amount of original variance explained by a new derived variable is:
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Eigenvalue
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Eigenvector
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Eigenvalues
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Eigenvectors
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The term representing weights showing how much each original variable contributes to each newly derived variable is:
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Eigenvalue
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Eigenvector
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Eigenvalues
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Eigenvectors
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The following information about PCA is True or False:
First principal component – the vector on which the most data variation can be projected.
Second principal component – vector perpendicular to the first, chosen so it contains as much of the remaining variation as possible.
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The following information about PCA is True or False:
First principal component – the vector on which the most data variation can be projected.
Second principal component – Second best possible vector, chosen to account as much variation as possible, but less good fit than the First.
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When to use PCA:
You have a set of ‘p’ [blank_start]____________[blank_end] variables.
You want to repackage their variance into ‘m’ components.
You want ‘m’ to be [blank_start]____[blank_end] ‘p’.
Each component could/should/might explain different things.
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continuous
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class
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nominal
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explanatory
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<
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≤ (<=)
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>
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≥ (>=)
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==
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Covariance or Correlation Matrix.
If units of x and y are different, use a [blank_start]____________[blank_end] matrix (as it standardises the units).
If units of x and y are the same (e.g. temperature) or with similar orders of magnitude, use a [blank_start]____________[blank_end] matrix (although you may need to standardise units).
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correlation
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covariance
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similarity
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dissimilarity
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In regards to the scree plot, a component with eigenvalue < 1 captured less than what?
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1 variable’s worth of variance
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1% of the total variance
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1 average component's worth of variance
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1% of the (1st) principal component's variance
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Rotations, orthogonal vs oblique.
Varimax is an example of [blank_start]____________[blank_end], meaning it [blank_start]________[blank_end] allow for factors to correlate.
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Orthogonal
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Oblique
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does not
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does