BIOL2022 L20 Fishing expiditions: PCA and CCA

Question 1 of 10

2

Select from the dropdown lists to complete the text.

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

Explanation

Question 2 of 10

2

Select from the dropdown lists to complete the text.

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

Explanation

Question 3 of 10

1

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

Select one of the following:

Eigenvalue
Eigenvector
Eigenvalues
Eigenvectors

Explanation

Question 4 of 10

1

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

Select one of the following:

Eigenvalue
Eigenvector
Eigenvalues
Eigenvectors

Explanation

Question 5 of 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.

Select one of the following:

True
False

Explanation

Question 6 of 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.

Select one of the following:

True
False

Explanation

Question 7 of 10

2

Select from the dropdown lists to complete the text.

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.

Explanation

Question 8 of 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).

Drag and drop to complete the text.

correlation

covariance

similarity

dissimilarity

Explanation

Question 9 of 10

1

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

Select one of the following:

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

Explanation

Question 10 of 10

1

	Created by Michael Jardine almost 6 years ago

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

Question 1 of 10

Select from the dropdown lists to complete the text.

Explanation

Question 2 of 10

Select from the dropdown lists to complete the text.

Explanation

Question 3 of 10

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

Select one of the following:

Explanation

Question 4 of 10

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

Select one of the following:

Explanation

Question 5 of 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.

Select one of the following:

Explanation

Question 6 of 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.

Select one of the following:

Explanation

Question 7 of 10

Select from the dropdown lists to complete the text.

Explanation

Question 8 of 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).

Drag and drop to complete the text.

Explanation

Question 9 of 10

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

Select one of the following:

Explanation

Question 10 of 10

Select from the dropdown lists to complete the text.

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.

Explanation

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.