PCA stands for [blank_start]________[blank_end] [blank_start]________[blank_end] [blank_start]________[blank_end].
Responda
Principle
Principal
Parametric
Post-hoc
Component
Components
Correlate
Correlates
Criterion
Criteria
Analysis
Analyses
Asshole
Arsehole
Array
Arrays
Questão 2
Questão
CCA stands for [blank_start]________[blank_end] [blank_start]________[blank_end] [blank_start]________[blank_end].
Responda
Canonical
Correlate
Correlation
Correlates
Correlations
Component
Components
Canonical
Component
Components
Correlate
Correlation
Correlates
Correlations
Criterion
Criteria
Analyses
Analysis
Questão 3
Questão
The term representing the amount of original variance explained by a new derived variable is:
Responda
Eigenvalue
Eigenvector
Eigenvalues
Eigenvectors
Questão 4
Questão
The term representing weights showing how much each original variable contributes to each newly derived variable is:
Responda
Eigenvalue
Eigenvector
Eigenvalues
Eigenvectors
Questão 5
Questão
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.
Responda
True
False
Questão 6
Questão
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.
Responda
True
False
Questão 7
Questão
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.
Responda
continuous
class
nominal
explanatory
<
≤ (<=)
>
≥ (>=)
==
Questão 8
Questão
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).
Responda
correlation
covariance
similarity
dissimilarity
Questão 9
Questão
In regards to the scree plot, a component with eigenvalue < 1 captured less than what?
Responda
1 variable’s worth of variance
1% of the total variance
1 average component's worth of variance
1% of the (1st) principal component's variance
Questão 10
Questão
Rotations, orthogonal vs oblique.
Varimax is an example of [blank_start]____________[blank_end], meaning it [blank_start]________[blank_end] allow for factors to correlate.
Responda
Orthogonal
Oblique
does not
does
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