Michael Jardine
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Module 3, Lecture 2 Outline: • Factor analysis • PCA • Case study – beer • Process for interpreting outputs

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Michael Jardine
Creado por Michael Jardine hace casi 6 años
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BIOL2022 L20 Fishing expiditions: PCA and CCA

Pregunta 1 de 10

2

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PCA stands for ( Principle, Principal, Parametric, Post-hoc ) ( Component, Components, Correlate, Correlates, Criterion, Criteria ) ( Analysis, Analyses, Asshole, Arsehole, Array, Arrays ).

Explicación

Pregunta 2 de 10

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CCA stands for ( Canonical, Correlate, Correlation, Correlates, Correlations, Component, Components ) ( Canonical, Component, Components, Correlate, Correlation, Correlates, Correlations, Criterion, Criteria ) ( Analyses, Analysis ).

Explicación

Pregunta 3 de 10

1

The term representing the amount of original variance explained by a new derived variable is:

Selecciona una de las siguientes respuestas posibles:

  • Eigenvalue

  • Eigenvector

  • Eigenvalues

  • Eigenvectors

Explicación

Pregunta 4 de 10

1

The term representing weights showing how much each original variable contributes to each newly derived variable is:

Selecciona una de las siguientes respuestas posibles:

  • Eigenvalue

  • Eigenvector

  • Eigenvalues

  • Eigenvectors

Explicación

Pregunta 5 de 10

1

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.

Selecciona uno de los siguientes:

  • VERDADERO
  • FALSO

Explicación

Pregunta 6 de 10

1

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.

Selecciona uno de los siguientes:

  • VERDADERO
  • FALSO

Explicación

Pregunta 7 de 10

2

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When to use PCA:
You have a set of ‘p’ ( continuous, class, nominal, explanatory ) variables.
You want to repackage their variance into ‘m’ components.
You want ‘m’ to be ( <, ≤ (<=), >, ≥ (>=), == ) ‘p’.
Each component could/should/might explain different things.

Explicación

Pregunta 8 de 10

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Covariance or Correlation Matrix.
If units of x and y are different, use a 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 matrix (although you may need to standardise units).

Arrastra y suelta para completar el texto.

    correlation
    covariance
    similarity
    dissimilarity

Explicación

Pregunta 9 de 10

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In regards to the scree plot, a component with eigenvalue < 1 captured less than what?

Selecciona una de las siguientes respuestas posibles:

  • 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

Explicación

Pregunta 10 de 10

1

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Rotations, orthogonal vs oblique.
Varimax is an example of ( Orthogonal, Oblique ), meaning it ( does not, does ) allow for factors to correlate.

Explicación

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