(Principal Component and Factor Analysis Final Exam, MCQ? Complete Exam Material 2026)
Principal Component and Factor Analysis Final Exam, MCQ
Study online at https://quizlet.com/_igepj2
1. Derived input direction methods, like principal All predictors are highly correlated,
component regression, are most helpful when: and you want to reduce the dimen-
sionality before regression
2. Which scenario best illustrates an advantage of When predictor variables are high-
using Principal Components Regression (PCR) ly correlated, reducing dimensionali-
in linear modeling? ty before regression improves predic-
tion stability.
3. Which best describes the real-world role of They show how strongly each origi-
principal component loadings in Principal Com- nal variable contributes to a principal
ponents Regression (PCR)? component.
4. In a practical PCR application, why is 'variance It indicates the proportion of the orig-
explained' by selected components important? inal data’s information captured by
the components.
5. Why is data preprocessing and feature engi- It transforms, normalizes, and creates
neering essential for effective machine learn- useful data features to help models
ing applications? learn patterns more effectively.
6. In image processing, scaling pixel intensities Normalization
between 0 and 1 best describes which prepro-
cessing technique?
7. If a psychologist computes the Pearson cor- There is a strong positive linear rela-
relation coefficient on two variables: number tionship between hours studied and
of hours studied and test scores, what does a test scores
value close to +1 indicate?
8. A psychologist collects data on hours studied Calculating the mean of each variable
and exam scores for 30 students. Which step is
3/31/2026 3/31/2026 3/31/2026
,(Principal Component and Factor Analysis Final Exam, MCQ? Complete Exam Material 2026)
Principal Component and Factor Analysis Final Exam, MCQ
Study online at https://quizlet.com/_igepj2
essential in computing the Pearson correlation
coefficient for these variables?
9. How does the Pearson correlation coefficient It is the normalized form of covariance
relate to covariance? using standard deviations
10. If you want to know whether students' test Covariance and correlation
scores in math and science tend to increase
together, which concept should you apply?
11. What does the correlation coefficient between The strength and direction of a linear
two discrete random variables measure? relationship between them
12. What best demonstrates the symmetry of co- Cov(X, Y) = Cov(Y, X)
variance for two discrete random variables X
and Y?
13. A researcher wants to understand how two dif- between-set covariance
ferent sets of variables relate to each other
rather than within themselves. Which matrix
describes this connection?
14. Why might a psychologist use factor analysis To identify underlying traits measured
when developing a new personality test? by test items.
15. Which estimation method is most commonly Maximum Likelihood.
selected in practical applications when data are
normally distributed and sample size is moder-
ate to large?
16. Which situation best illustrates the use of the When a researcher wants to estimate
Principal Factor Method in factor analysis? factor loadings using only the shared
variance among variables.
3/31/2026 3/31/2026 3/31/2026
Principal Component and Factor Analysis Final Exam, MCQ
Study online at https://quizlet.com/_igepj2
1. Derived input direction methods, like principal All predictors are highly correlated,
component regression, are most helpful when: and you want to reduce the dimen-
sionality before regression
2. Which scenario best illustrates an advantage of When predictor variables are high-
using Principal Components Regression (PCR) ly correlated, reducing dimensionali-
in linear modeling? ty before regression improves predic-
tion stability.
3. Which best describes the real-world role of They show how strongly each origi-
principal component loadings in Principal Com- nal variable contributes to a principal
ponents Regression (PCR)? component.
4. In a practical PCR application, why is 'variance It indicates the proportion of the orig-
explained' by selected components important? inal data’s information captured by
the components.
5. Why is data preprocessing and feature engi- It transforms, normalizes, and creates
neering essential for effective machine learn- useful data features to help models
ing applications? learn patterns more effectively.
6. In image processing, scaling pixel intensities Normalization
between 0 and 1 best describes which prepro-
cessing technique?
7. If a psychologist computes the Pearson cor- There is a strong positive linear rela-
relation coefficient on two variables: number tionship between hours studied and
of hours studied and test scores, what does a test scores
value close to +1 indicate?
8. A psychologist collects data on hours studied Calculating the mean of each variable
and exam scores for 30 students. Which step is
3/31/2026 3/31/2026 3/31/2026
,(Principal Component and Factor Analysis Final Exam, MCQ? Complete Exam Material 2026)
Principal Component and Factor Analysis Final Exam, MCQ
Study online at https://quizlet.com/_igepj2
essential in computing the Pearson correlation
coefficient for these variables?
9. How does the Pearson correlation coefficient It is the normalized form of covariance
relate to covariance? using standard deviations
10. If you want to know whether students' test Covariance and correlation
scores in math and science tend to increase
together, which concept should you apply?
11. What does the correlation coefficient between The strength and direction of a linear
two discrete random variables measure? relationship between them
12. What best demonstrates the symmetry of co- Cov(X, Y) = Cov(Y, X)
variance for two discrete random variables X
and Y?
13. A researcher wants to understand how two dif- between-set covariance
ferent sets of variables relate to each other
rather than within themselves. Which matrix
describes this connection?
14. Why might a psychologist use factor analysis To identify underlying traits measured
when developing a new personality test? by test items.
15. Which estimation method is most commonly Maximum Likelihood.
selected in practical applications when data are
normally distributed and sample size is moder-
ate to large?
16. Which situation best illustrates the use of the When a researcher wants to estimate
Principal Factor Method in factor analysis? factor loadings using only the shared
variance among variables.
3/31/2026 3/31/2026 3/31/2026
