PCA EXAM 3 & 4 (Georgia Exam) Questions With 100%
Correct Verified Answers Latest Updated 2026/2027
(GRADED A+)
What is principal component analysis? - VERIFIED ANSWER - PCA is a
multivariate statistical technique to reduce higher dimensional data to
lower dimensions, remove noise, and extract crucial information such as
features and attributes from large amounts of data.
What are the general objectives of principal component analysis? -
VERIFIED ANSWER - Data reduction and interpretation.
Does principal component analysis separate dependent and independent
variables? - VERIFIED ANSWER - No, there is no separation into dependent
and independent variables.
What does principal component analysis transform correlated variables
into? - VERIFIED ANSWER - A smaller set of uncorrelated variables called
principal components.
,What is a common use of principal component analysis? - VERIFIED
ANSWER - It is often used as the first step in factor analysis.
Is PCA supervised or unsupervised? - VERIFIED ANSWER - unsupervised
What does PCA transform correlated variables into - VERIFIED ANSWER -
Transforms them into uncorrelated variables
What does a longer arrow mean in PCA - VERIFIED ANSWER - A larger
eigenvalue, meaning it's direction explains more of the data's total
variance.
PCA Projection Principle - VERIFIED ANSWER - Project data onto a lower
dimensional space that minimizes the loss of projected data and
maximizes the information between data point and projection
Standardization PCA - VERIFIED ANSWER - 1st step of PCA, scale each
feature to have a mean of 0 and a standard deviation of 1, ensuring all
variables contribute equally
Steps of PCA algorithm - VERIFIED ANSWER - 1. Normalize data.
,2. Calculate the covariance matrix of normalized data.
3. Calculate the eigenvalues and eigenvectors of the calculated
covariance matrix.
4. Project Data onto New Axes
What does calculating the Covaraince Matrix do? - VERIFIED ANSWER -
Matrix reveals how each variable in the dataset relates to every other
variable , revealing correlations and variance structures
Eigenvectors - VERIFIED ANSWER - Vectors indicating directions of
maximum variance. Doesn't change direction when transformation is
applied
Eigenvalues and Eigenvectors - VERIFIED ANSWER - Values and vectors
associated with linear transformations. Measure the mvariance they
capture, determine maximum data spread
Eigenvalues - VERIFIED ANSWER - Scalars indicating how much
eigenvectors are stretched or shrunk.
, Project data onto new axes - VERIFIED ANSWER - transforming the original
data into new, lower-dimensional space defined by the selected
components. This is how the data is simplified and keeps the most
significant information
How to select the right number of components - VERIFIED ANSWER - Use a
scree plot to plot eigenvalue and the elbow is the determent.
Check cumulative variance and use the fewest components that explain a
large proportion of the total variance
Apply Eigenvalue threshold, only keeping values greater than one
What is the elbow in a scree plot - VERIFIED ANSWER - The point where
adding components yields diminishing returns. The point in the graph
where the more you add the more it declines
Why is normalization important before applying PCA? - VERIFIED ANSWER -
The variance will dominate all the results of your PCA, normalization will
allow everything to equally contribute to the variance, results will be
more balanced.
Correct Verified Answers Latest Updated 2026/2027
(GRADED A+)
What is principal component analysis? - VERIFIED ANSWER - PCA is a
multivariate statistical technique to reduce higher dimensional data to
lower dimensions, remove noise, and extract crucial information such as
features and attributes from large amounts of data.
What are the general objectives of principal component analysis? -
VERIFIED ANSWER - Data reduction and interpretation.
Does principal component analysis separate dependent and independent
variables? - VERIFIED ANSWER - No, there is no separation into dependent
and independent variables.
What does principal component analysis transform correlated variables
into? - VERIFIED ANSWER - A smaller set of uncorrelated variables called
principal components.
,What is a common use of principal component analysis? - VERIFIED
ANSWER - It is often used as the first step in factor analysis.
Is PCA supervised or unsupervised? - VERIFIED ANSWER - unsupervised
What does PCA transform correlated variables into - VERIFIED ANSWER -
Transforms them into uncorrelated variables
What does a longer arrow mean in PCA - VERIFIED ANSWER - A larger
eigenvalue, meaning it's direction explains more of the data's total
variance.
PCA Projection Principle - VERIFIED ANSWER - Project data onto a lower
dimensional space that minimizes the loss of projected data and
maximizes the information between data point and projection
Standardization PCA - VERIFIED ANSWER - 1st step of PCA, scale each
feature to have a mean of 0 and a standard deviation of 1, ensuring all
variables contribute equally
Steps of PCA algorithm - VERIFIED ANSWER - 1. Normalize data.
,2. Calculate the covariance matrix of normalized data.
3. Calculate the eigenvalues and eigenvectors of the calculated
covariance matrix.
4. Project Data onto New Axes
What does calculating the Covaraince Matrix do? - VERIFIED ANSWER -
Matrix reveals how each variable in the dataset relates to every other
variable , revealing correlations and variance structures
Eigenvectors - VERIFIED ANSWER - Vectors indicating directions of
maximum variance. Doesn't change direction when transformation is
applied
Eigenvalues and Eigenvectors - VERIFIED ANSWER - Values and vectors
associated with linear transformations. Measure the mvariance they
capture, determine maximum data spread
Eigenvalues - VERIFIED ANSWER - Scalars indicating how much
eigenvectors are stretched or shrunk.
, Project data onto new axes - VERIFIED ANSWER - transforming the original
data into new, lower-dimensional space defined by the selected
components. This is how the data is simplified and keeps the most
significant information
How to select the right number of components - VERIFIED ANSWER - Use a
scree plot to plot eigenvalue and the elbow is the determent.
Check cumulative variance and use the fewest components that explain a
large proportion of the total variance
Apply Eigenvalue threshold, only keeping values greater than one
What is the elbow in a scree plot - VERIFIED ANSWER - The point where
adding components yields diminishing returns. The point in the graph
where the more you add the more it declines
Why is normalization important before applying PCA? - VERIFIED ANSWER -
The variance will dominate all the results of your PCA, normalization will
allow everything to equally contribute to the variance, results will be
more balanced.
