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BIOL2022 L20 Fishing expiditions: PCA and CCA

Usuario eliminado 10 preguntas Actualizado el 12 nov 2018

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Preguntas del test (10)

  1. 1PCA stands for [blank_start]________[blank_end] [blank_start]________[blank_end] [blank_start]________[blank_end].
    1. APrinciple
    2. BPrincipal
    3. CParametric
    4. DPost-hoc
    5. EComponent
    6. FComponents
    7. GCorrelate
    8. HCorrelates
    9. ICriterion
    10. JCriteria
    11. KAnalysis
    12. LAnalyses
    13. MAsshole
    14. NArsehole
    15. OArray
    16. PArrays
  2. 2CCA stands for [blank_start]________[blank_end] [blank_start]________[blank_end] [blank_start]________[blank_end].
    1. ACanonical
    2. BCorrelate
    3. CCorrelation
    4. DCorrelates
    5. ECorrelations
    6. FComponent
    7. GComponents
    8. HCanonical
    9. IComponent
    10. JComponents
    11. KCorrelate
    12. LCorrelation
    13. MCorrelates
    14. NCorrelations
    15. OCriterion
    16. PCriteria
    17. QAnalyses
    18. RAnalysis
  3. 3The term representing the amount of original variance explained by a new derived variable is:
    1. AEigenvalue
    2. BEigenvector
    3. CEigenvalues
    4. DEigenvectors
  4. 4The term representing weights showing how much each original variable contributes to each newly derived variable is:
    1. AEigenvalue
    2. BEigenvector
    3. CEigenvalues
    4. DEigenvectors
  5. 5The 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.
    1. AVerdadero
    2. BFalso
  6. 6The 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.
    1. AVerdadero
    2. BFalso
  7. 7When 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.
    1. Acontinuous
    2. Bclass
    3. Cnominal
    4. Dexplanatory
    5. E<
    6. F≤ (<=)
    7. G>
    8. H≥ (>=)
    9. I==
  8. 8Covariance 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).
    1. Acorrelation
    2. Bcovariance
    3. Csimilarity
    4. Ddissimilarity
  9. 9In regards to the scree plot, a component with eigenvalue < 1 captured less than what?
    1. A1 variable’s worth of variance
    2. B1% of the total variance
    3. C1 average component's worth of variance
    4. D1% of the (1st) principal component's variance
  10. 10Rotations, orthogonal vs oblique.
    Varimax is an example of [blank_start]____________[blank_end], meaning it [blank_start]________[blank_end] allow for factors to correlate.
    1. AOrthogonal
    2. BOblique
    3. Cdoes not
    4. Ddoes

Información

Descripción
Module 3, Lecture 2 Outline: • Factor analysis • PCA • Case study – beer • Process for interpreting outputs
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Creado el 11 nov 2018 · actualizado el 12 nov 2018
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