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Machine Learning – Dimensionality Reduction Cognitive class Exam Answers:-

Course Name :- Machine Learning – Dimensionality Reduction

Module 1:- Data Series

Question 1 : Which of the following techniques can be used to reduce the dimensions of the population?

  • Exploratory Data Analysis
  • Principal Component Analysis
  • Exploratory Factor Analysis
  • Cluster Analysis

Question 2: Cluster Analysis partitions the columns of the data, whereas principal component and exploratory factor analyses partition the rows of the data. True or false?

  • False
  • True

Question 3:  Which of the following options are true? Select all that apply.

  • PCA explains the total variance
  • EFA explains the common variance
  • EFA identifies measures that are sufficiently similar to each other to justify combination
  • PCA captures latent constructs that are assumed to cause variance

Module 2 : -Data Refinement

Question 1 : Which of the following options is true?

  • A matrix of correlations describes all possible pairwise relationships
  • Eigenvalues are the principal components
  • Correlation does not explain the covariation between two vectors
  • Eigenvectors are a measure of total variance, as explained by the principal components

Question 2: PCA is a method to reduce your data to the fewest ‘principal components’ while maximizing the variance explained. True or false?

  • False
  • True

Question 3 : Which of the following techniques was NOT covered in this lesson?

  • Parallel analysis
  • Percentage of Common Variance
  • Scree Test
  • Kaiser-Guttman Rule

Module 3 :- Exploring Data

Question 1: EFA is commonly used in which of the following applications? Select all that apply.

  • Customer satisfaction surveys
  • Personality tests
  • Performance evaluations
  • Image analysis

Question 2 : Which of the following options is an example of an Oblique Rotation?

  • Regmax
  • Varimax
  • Softmax
  • Promax

Question 3 : An Orthogonal Rotation assumes that factors are correlated with each other. True or false?

  • False
  • True

Machine Learning – Dimensionality Reduction Cognitive class final Exam Answers:-

Question 1: Why might you use cluster analysis as an analytic strategy?

  • To identify higher-order dimensions
  • To identify outliers
  • To reduce the number of variables
  • To segment the market
  • None of the above

Question 2: Suppose you have 100,000 individuals in a dataset, and each individual varies along 60 dimensions. On average, the dimensions are correlated at r = .45. You want to group the variables together, so you decide to run principle component analysis. How many meaningful, higher-order components can you extract?

  • 60
  • 3
  • 20
  • 24

Question 3 :  What technique should you use to identify the dimensions that hang together?

  • Principal axis factoring
  • Confirmatory factor analysis
  • Exploratory factor analysis
  • Two of the above
  • None of the above

Question 4 : What are loadings?

  • Covariance between the two factors
  • Correlations between each variable and its factor
  • Correlations between each variable and its component
  • Two of the above
  • None of the above

Question 5  : When would you use PCA over EFA?

  • When you want to use an orthogonal rotation
  • When you are interested in explaining the total variance in a variance-covariance matrix
  • When you have too many variables
  • When you are interested in a latent construct
  • None of the above

Question 6 : What is uniqueness?

  • A measure of replicability of the factor
  • The amount of variance not explained by the factor structure
  • The amount of variance explained by the factor structure
  • The amount of variance explained by the factor
  • None of the above

Question 7 : Suppose you are looking to extract the major dimensions of a parrot’s personality. Which technique would you use?

  • Maximum likelihood
  • Principal component analysis
  • Cluster analysis
  • Factor analysis
  • None of the above

Question 8 : Suppose you have 60 variables in a dataset, and you know that 2 components explain the data very well. How many components can you extract?

  • 45
  • 5
  • 60
  • 2
  • None of the above

Question 9 : When would you use an orthogonal rotation?

  • When correlations between the variables are large
  • When you observe small correlations between the variables in the dataset
  • When you think that the factors are uncorrelated
  • All of the above
  • None of the above

Question 10 : When would you use confirmatory factor analysis?

  • When you want to validate the factor solution
  • When you want to explain the variance in the matrix accounting for the measurement error
  • When you want to identify the factors
  • Two of the above
  • None of the above

Question 11 : Which of the following is NOT a rule when deciding on the number of factors?

  • Newman-Frank Test
  • Percentage of common variance explained
  • Scree test
  • Kaiser-Guttman
  • None of the above

Question 12 : What is one assumption of factor analysis?

  • A number of factors can be determined via the Scree test
  • Factor analysis will extract only unique factors
  • A latent variable causes the variance in observed variables
  • There is no measurement error
  • None of the above

Question 13 : What is an eigenvector?

  • The proportion of the variance explained in the matrix
  • A higher-order dimension that subsumes all of the lower-order errors
  • A higher-order dimension that subsumes similar lower-order dimensions
  • A higher-order dimension that subsumes all lower-order dimensions
  • None of the above

Question 14 : What is a promax rotation?

  • A rotation method that minimizes the square loadings on each factor
  • A rotation method that maximizes the variance explained
  • A rotation method that maximizes the square loadings on each factor
  • A rotation method that minimizes the variance explained
  • None of the above

Question 15 : What is the cut-off point for the Common Variance Explained rule?

  • 80% of variance explained
  • 50% of variance explained
  • 3 variables
  • 1 unit
  • None of the above

Question 16 : Why would you try to reduce dimensions?

  • Individuals need to be placed into groups
  • Variables are highly-correlated
  • Many variables are likely assessing the same thing
  • Two of the above
  • All of the above

Question 17: If you have 20 variables in a dataset, how many dimensions are there?

  • At most 20
  • At least 20
  • As many as the number of factors you can extract
  • Not enough information
  • None of the above

Question 18 : What term describes the amount of variance of each variable explained by the factor structure?

  • Eigenvector
  • Commonality
  • Similarity
  • Communality
  • None of the above

Question 19: What package contains the necessary functions to perform PCA and EFA?

  • ggplot2
  • FA
  • psych
  • factAnalis
  • None of the above

Question 20 : What is the best method for identifying the number of factors to extract?

  • Parallel Analysis
  • Scree test
  • Newman-Frank Test
  • Percentage of common variance explained
  • All of the above

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