BIOL2022 L20 Fishing expiditions: PCA and CCA

Questão 1 de 10

2

Selecione das listas do Menu para completar o texto.

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

Explicação

Questão 2 de 10

2

Selecione das listas do Menu para completar o texto.

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

Explicação

Questão 3 de 10

1

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

Selecione uma das seguintes:

Eigenvalue
Eigenvector
Eigenvalues
Eigenvectors

Explicação

Questão 4 de 10

1

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

Selecione uma das seguintes:

Eigenvalue
Eigenvector
Eigenvalues
Eigenvectors

Explicação

Questão 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.

Selecione uma das opções:

VERDADEIRO
FALSO

Explicação

Questão 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.

Selecione uma das opções:

VERDADEIRO
FALSO

Explicação

Questão 7 de 10

2

Selecione das listas do Menu para completar o texto.

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.

Explicação

Questão 8 de 10

1

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).

Arraste e solte para completar o texto.

correlation

covariance

similarity

dissimilarity

Explicação

Questão 9 de 10

1

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

Selecione uma das seguintes:

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

Explicação

Questão 10 de 10

1

	Criado por Michael Jardine quase 6 anos atrás

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

BIOL2022 L20 Fishing expiditions: PCA and CCA

BIOL2022 L20 Fishing expiditions: PCA and CCA

Questão 1 de 10

Selecione das listas do Menu para completar o texto.

Explicação

Questão 2 de 10

Selecione das listas do Menu para completar o texto.

Explicação

Questão 3 de 10

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

Selecione uma das seguintes:

Explicação

Questão 4 de 10

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

Selecione uma das seguintes:

Explicação

Questão 5 de 10

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.

Selecione uma das opções:

Explicação

Questão 6 de 10

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.

Selecione uma das opções:

Explicação

Questão 7 de 10

Selecione das listas do Menu para completar o texto.

Explicação

Questão 8 de 10

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).

Arraste e solte para completar o texto.

Explicação

Questão 9 de 10

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

Selecione uma das seguintes:

Explicação

Questão 10 de 10

Selecione das listas do Menu para completar o texto.

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

Explicação

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.

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.

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).

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