1What is the main purpose of classification in multivariate analysis?
Classification
Easy
A.To calculate averages for each variable
B.To assign cases to predefined groups
C.To remove variables from a dataset
D.To arrange observations by date
Correct Answer: To assign cases to predefined groups
Explanation:
Classification assigns observations or cases to groups whose categories are already defined.
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2Which type of data is normally required to train a supervised classification model?
Classification
Easy
A.Data containing one observation
B.Data containing no variables
C.Data with known class labels
D.Data with missing class labels
Correct Answer: Data with known class labels
Explanation:
Supervised classification learns from observations that already have known class labels.
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3In a classification problem, the dependent variable is usually:
Classification
Easy
A.A correlation coefficient
B.A random error term
C.A categorical variable
D.A continuous ratio
Correct Answer: A categorical variable
Explanation:
The dependent variable represents categories or classes, such as approved and rejected.
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4What is the basic objective of factor analysis?
Important methods of factor analysis
Easy
A.To rank individual observations
B.To forecast a time series
C.To compare group percentages
D.To identify underlying factors
Correct Answer: To identify underlying factors
Explanation:
Factor analysis identifies a smaller number of latent factors underlying a larger set of related variables.
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5Principal component analysis primarily explains which type of variance?
Important methods of factor analysis
Easy
A.Total variance
B.Error variance only
C.Between-group variance only
D.Common variance only
Correct Answer: Total variance
Explanation:
Principal component analysis uses the total variance present in the observed variables.
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6Principal axis factoring mainly focuses on:
Important methods of factor analysis
Easy
A.Mean differences among groups
B.Common variance among variables
C.Total variance among variables
D.Distances among cluster centers
Correct Answer: Common variance among variables
Explanation:
Principal axis factoring extracts factors from the common variance shared by observed variables.
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7Which factor extraction method estimates parameters by maximizing a likelihood function?
Important methods of factor analysis
Easy
A.Ward's method
B.Maximum likelihood
C.Varimax rotation
D.Multidimensional scaling
Correct Answer: Maximum likelihood
Explanation:
Maximum likelihood estimates factor model parameters by maximizing the likelihood of the observed data.
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8Which matrix is commonly examined at the beginning of factor analysis?
Factor analysis procedure
Easy
A.Correlation matrix
B.Payoff matrix
C.Distance matrix
D.Confusion matrix
Correct Answer: Correlation matrix
Explanation:
The correlation matrix shows whether variables are sufficiently related for factor analysis.
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9What does the Kaiser-Meyer-Olkin (KMO) measure assess?
Factor analysis procedure
Easy
A.Classification accuracy
B.Sampling adequacy
C.Cluster separation
D.Preference strength
Correct Answer: Sampling adequacy
Explanation:
The KMO measure assesses whether the pattern of correlations is suitable for factor analysis.
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10According to the Kaiser criterion, which factors are generally retained?
Factor analysis procedure
Easy
A.Factors with eigenvalues above
B.Factors with eigenvalues below
C.Factors with loadings equal to
D.Factors with communalities below
Correct Answer: Factors with eigenvalues above
Explanation:
The Kaiser criterion generally retains factors whose eigenvalues are greater than .
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11Why is factor rotation performed?
Rotation in factor analysis
Easy
A.To create new observations
B.To increase the sample size
C.To improve factor interpretability
D.To remove all correlations
Correct Answer: To improve factor interpretability
Explanation:
Rotation produces a simpler factor-loading pattern, making the factors easier to interpret.
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12Which of the following is an orthogonal rotation method?
Rotation in factor analysis
Easy
A.Varimax
B.Oblimin
C.Quartimin
D.Promax
Correct Answer: Varimax
Explanation:
Varimax is an orthogonal rotation method that keeps the rotated factors uncorrelated.
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13What does an oblique rotation allow?
Rotation in factor analysis
Easy
A.Factors to be correlated
B.Observations to form clusters
C.Factors to remain identical
D.Variables to become categorical
Correct Answer: Factors to be correlated
Explanation:
Oblique rotation allows relationships or correlations among the extracted factors.
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14What is the main objective of cluster analysis?
Overview of cluster analysis
Easy
A.To estimate factor loadings
B.To group similar observations
C.To predict a labeled category
D.To measure consumer utility
Correct Answer: To group similar observations
Explanation:
Cluster analysis places similar observations in the same group and dissimilar observations in different groups.
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15Which diagram commonly displays the results of hierarchical clustering?
Overview of cluster analysis
Easy
A.Scatter matrix
B.Perceptual map
C.Scree plot
D.Dendrogram
Correct Answer: Dendrogram
Explanation:
A dendrogram is a tree-like diagram showing how observations or clusters are joined.
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16What must usually be specified before applying the -means clustering method?
Overview of cluster analysis
Easy
A.The utility scores
B.The class labels
C.The number of clusters
D.The number of factors
Correct Answer: The number of clusters
Explanation:
The -means method requires the analyst to specify the desired number of clusters, represented by .
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17What is discriminant analysis mainly used to predict?
Discriminant analysis
Easy
A.Membership in unknown clusters
B.Positions on a perceptual map
C.Membership in predefined groups
D.Utilities of product attributes
Correct Answer: Membership in predefined groups
Explanation:
Discriminant analysis uses predictor variables to classify cases into predefined groups.
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18A discriminant function is usually formed as:
Discriminant analysis
Easy
A.A matrix of preference ranks
B.A graph of factor eigenvalues
C.A list of cluster labels
D.A linear combination of predictors
Correct Answer: A linear combination of predictors
Explanation:
A discriminant function combines predictor variables, often linearly, to distinguish between groups.
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19What does multidimensional scaling commonly produce?
Multidimensional scaling
Easy
A.A hierarchy of clusters
B.A spatial map of objects
C.A list of class predictions
D.A table of factor loadings
Correct Answer: A spatial map of objects
Explanation:
Multidimensional scaling represents similarities or dissimilarities among objects as distances on a spatial map.
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20What is conjoint analysis primarily used to study?
Conjoint analysis
Easy
A.Preferences for product attributes
B.Correlations among latent factors
C.Distances between cluster centers
D.Differences between class labels
Correct Answer: Preferences for product attributes
Explanation:
Conjoint analysis estimates how consumers value the attributes and levels that make up a product or service.
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21A researcher wants to predict customer loyalty from satisfaction, perceived value, and service quality. How should this multivariate problem be classified?
Classification
Medium
A.An interdependence technique with no criterion variable
B.An interdependence technique based only on distances
C.A dependence technique with several criterion variables
D.A dependence technique with one criterion variable
Correct Answer: A dependence technique with one criterion variable
Explanation:
Customer loyalty is the single criterion variable, while the other variables serve as predictors. The problem therefore requires a dependence technique.
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22A study examines 15 attitude variables without specifying any variable as an outcome and seeks a smaller set of underlying dimensions. Which classification is most appropriate?
Classification
Medium
A.A dependence method for prediction
B.An interdependence method for case classification
C.A dependence method for group comparison
D.An interdependence method for data reduction
Correct Answer: An interdependence method for data reduction
Explanation:
No variable is designated as dependent, and the goal is to identify latent dimensions. This is an interdependence and data-reduction problem.
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23A researcher wants to summarize the total variance in 12 observed variables into a few weighted composites. Which method is most suitable?
Important methods of factor analysis
Medium
A.Principal axis factoring
B.Canonical discriminant analysis
C.Maximum likelihood factoring
D.Principal component analysis
Correct Answer: Principal component analysis
Explanation:
Principal component analysis uses total observed variance and is appropriate when the main objective is data reduction.
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24A researcher is interested in latent constructs that explain only the shared variance among measured variables. Which method best matches this objective?
Important methods of factor analysis
Medium
A.Hierarchical cluster analysis
B.Principal axis factoring
C.Multidimensional scaling
D.Principal component analysis
Correct Answer: Principal axis factoring
Explanation:
Principal axis factoring estimates factors from common variance and excludes unique and error variance from the factor model.
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25A researcher wants to test the statistical fit of a factor model and construct confidence intervals, assuming approximate multivariate normality. Which extraction method is most appropriate?
Important methods of factor analysis
Medium
A.Maximum likelihood extraction
B.Principal component extraction
C.Centroid factor extraction
D.Unweighted least-squares extraction
Correct Answer: Maximum likelihood extraction
Explanation:
Maximum likelihood extraction permits statistical tests of model fit and parameter inference when its distributional assumptions are reasonably satisfied.
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26A dataset produces a Kaiser-Meyer-Olkin value of and a significant Bartlett's test with . What is the best conclusion?
Factor analysis procedure
Medium
A.The factors require an oblique rotation
B.The variables have perfect multicollinearity
C.The data are suitable for factor analysis
D.The sample must be divided into clusters
Correct Answer: The data are suitable for factor analysis
Explanation:
A KMO value of indicates good sampling adequacy, while significant Bartlett's test shows that the correlation matrix is not an identity matrix.
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27After extraction, one variable has a communality of , while all other variables have communalities above . What does the value indicate?
Factor analysis procedure
Medium
A.The retained factors explain little variance in that variable
B.The retained factors explain of that variable's variance
C.The variable has a loading of on every factor
D.The variable explains little variance in the retained factors
Correct Answer: The retained factors explain little variance in that variable
Explanation:
A communality of means that the retained factors account for only of the observed variance in that variable.
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28The first six eigenvalues from a correlation matrix are , , , , , and . Under the Kaiser criterion, how many factors should initially be retained?
Factor analysis procedure
Medium
A.Three factors
B.Six factors
C.Four factors
D.Two factors
Correct Answer: Three factors
Explanation:
The Kaiser criterion retains factors with eigenvalues greater than . Only the first three eigenvalues meet this condition.
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29A theory states that anxiety and depression are related latent constructs. Which rotation is most appropriate when extracting these factors?
Rotation in factor analysis
Medium
A.Quartimax rotation
B.Oblimin rotation
C.Varimax rotation
D.Equamax rotation
Correct Answer: Oblimin rotation
Explanation:
Oblimin is an oblique rotation, so it allows the extracted factors to correlate as expected by the theory.
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30After varimax rotation, a variable has loadings of , , and on three factors. How should the variable usually be interpreted?
Rotation in factor analysis
Medium
A.It represents all three factors equally
B.It primarily represents the first factor
C.It primarily represents the second factor
D.It primarily represents the third factor
Correct Answer: It primarily represents the first factor
Explanation:
The loading of is substantially larger than the other loadings, so the variable is mainly associated with the first factor.
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31A retailer knows in advance that customers must be divided into four market segments and has a large numerical dataset. Which method is most appropriate?
Overview of cluster analysis
Medium
A.Discriminant analysis with four predictors
B.Factor analysis with four factors
C.K-means clustering with
D.Single-linkage clustering with four cases
Correct Answer: K-means clustering with
Explanation:
K-means is suitable for large numerical datasets when the desired number of clusters is specified in advance.
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32Income is measured in dollars and age is measured in years. Before clustering customers using Euclidean distance, what should the researcher generally do?
Overview of cluster analysis
Medium
A.Convert both variables to ranks
B.Reverse-score the variables
C.Remove the variable with less variance
D.Standardize the variables
Correct Answer: Standardize the variables
Explanation:
Standardization prevents a variable with a larger numerical scale, such as income, from dominating the distance calculation.
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33A researcher uses single-linkage hierarchical clustering and obtains a long sequence of cases joined through close neighbors. Which known tendency does this illustrate?
Overview of cluster analysis
Medium
A.The rotation effect
B.The chaining effect
C.The suppression effect
D.The scaling effect
Correct Answer: The chaining effect
Explanation:
Single linkage can connect clusters through successive pairs of nearby observations, producing elongated clusters known as the chaining effect.
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34A bank has historical data in which applicants are already labeled as low, medium, or high credit risk. It wants to classify new applicants using financial ratios. Which technique is appropriate?
Discriminant analysis
Medium
A.Hierarchical cluster analysis
B.Exploratory factor analysis
C.Multiple discriminant analysis
D.Metric multidimensional scaling
Correct Answer: Multiple discriminant analysis
Explanation:
The outcome consists of three known groups, and the predictors are metric variables. Multiple discriminant analysis is designed for this classification task.
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35A discriminant function for two groups is . If an observation has and , what is its discriminant score?
Discriminant analysis
Medium
A.
B.
C.
D.
Correct Answer:
Explanation:
Substitution gives .
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36Box's M test is significant in a discriminant analysis involving several groups. Which assumption is most directly questioned?
Discriminant analysis
Medium
A.Independence of group memberships
B.Equality of covariance matrices
C.Equality of group sample sizes
D.Linearity of factor loadings
Correct Answer: Equality of covariance matrices
Explanation:
Box's M test evaluates whether the predictor covariance matrices are equal across the groups.
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37Consumers rate the pairwise dissimilarity of six smartphone brands. A researcher wants a two-dimensional map in which similar brands appear close together. Which technique should be used?
Multidimensional scaling
Medium
A.Principal axis factoring
B.Multidimensional scaling
C.Two-stage cluster sampling
D.Multiple discriminant analysis
Correct Answer: Multidimensional scaling
Explanation:
Multidimensional scaling represents similarity or dissimilarity data spatially, placing more similar objects closer together.
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38A two-dimensional multidimensional scaling solution has Stress , while a one-dimensional solution has Stress . What is the most reasonable interpretation?
Multidimensional scaling
Medium
A.Both solutions reproduce the distances equally well
B.The one-dimensional solution reproduces the distances better
C.The two-dimensional solution reproduces the distances better
D.Stress cannot be used to compare these solutions
Correct Answer: The two-dimensional solution reproduces the distances better
Explanation:
Lower Stress indicates a smaller discrepancy between observed dissimilarities and fitted distances, so the two-dimensional solution fits better.
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39In a conjoint study of laptops, a respondent's part-worths are for a low price, for a high-resolution screen, and for a light weight. Ignoring the intercept, what is the total utility of a laptop with all three features?
Conjoint analysis
Medium
A.
B.
C.
D.
Correct Answer:
Explanation:
Under the additive conjoint model, total utility is the sum of the selected part-worths: .
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40In a conjoint analysis, the utility range for price is , for brand is , and for warranty is . What is the relative importance of price?
Conjoint analysis
Medium
A.
B.
C.
D.
Correct Answer:
Explanation:
Relative importance equals the attribute's utility range divided by the sum of all ranges: , or .
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41A researcher has 12 continuous variables and wants to assign observations to one of four groups. The group labels are known for a training sample, but the group covariance matrices may differ. Which classification is most appropriate?
Classification
Hard
A.Supervised classification using quadratic discriminant analysis
B.Unsupervised classification using Ward's clustering
C.Unsupervised classification using principal component analysis
D.Supervised classification using linear discriminant analysis
Correct Answer: Supervised classification using quadratic discriminant analysis
Explanation:
Known training labels make the task supervised. Quadratic discriminant analysis accommodates group-specific covariance matrices, unlike linear discriminant analysis.
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42A binary classifier estimates . A false negative costs 8 units, a false positive costs 2 units, and correct decisions have zero cost. Under minimum expected loss, how should the observation be classified?
Classification
Hard
A.Classify as 1 because
B.Classify as 0 because
C.Classify as 0 because
D.Classify as 1 because
Correct Answer: Classify as 1 because
Explanation:
Predict class 1 when , which gives . Since , class 1 minimizes expected loss.
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43Which statement correctly distinguishes principal component analysis from principal axis factoring when both are applied to the same correlation matrix?
Important methods of factor analysis
Hard
A.PCA models only common variance, while principal axis factoring models total observed variance
B.PCA requires multivariate normality, while principal axis factoring always requires interval data
C.PCA initially uses unit diagonal entries, while principal axis factoring uses communality estimates
D.PCA estimates unique variances, while principal axis factoring fixes all unique variances at zero
Correct Answer: PCA initially uses unit diagonal entries, while principal axis factoring uses communality estimates
Explanation:
PCA decomposes total variance and therefore starts with ones on a correlation matrix diagonal. Principal axis factoring replaces the diagonal with estimated communalities to target common variance.
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44A dataset contains strongly non-normal indicators, and the researcher wants latent common factors without relying on a multivariate-normal likelihood-ratio test. Which extraction method is most defensible?
Important methods of factor analysis
Hard
A.Unweighted least squares PCA
B.Canonical discriminant factoring
C.Principal axis factoring
D.Maximum likelihood factoring
Correct Answer: Principal axis factoring
Explanation:
Principal axis factoring focuses on common variance and is less dependent on multivariate normality than maximum likelihood factor analysis.
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45Under maximum likelihood factor analysis, which result can be formally evaluated when its assumptions are sufficiently satisfied?
Important methods of factor analysis
Hard
A.Whether the observed variables form mutually exclusive classification groups
B.Whether every retained factor explains more variance than every observed variable
C.Whether rotated factor scores are uniquely determined for every observation
D.Whether a specified number of common factors adequately fits the covariance matrix
Correct Answer: Whether a specified number of common factors adequately fits the covariance matrix
Explanation:
Maximum likelihood factor analysis permits statistical tests and fit comparisons for models with specified numbers of factors, subject to distributional and identification assumptions.
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46For three standardized variables, the inverse correlation matrix is
Using , what additional operation is required before computing the KMO values?
Factor analysis procedure
Hard
A.Rotate the inverse matrix using an orthogonal criterion
B.Replace inverse-matrix diagonals with estimated communalities
C.Standardize each inverse-matrix row to unit variance
D.Convert inverse-matrix off-diagonals into partial correlations
Correct Answer: Convert inverse-matrix off-diagonals into partial correlations
Explanation:
KMO uses ordinary correlations and partial correlations. From , the partial correlation is .
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47A sample correlation matrix has eigenvalues . The corresponding mean eigenvalues from parallel random datasets are . How many factors should parallel analysis retain?
Factor analysis procedure
Hard
A.Three factors
B.Four factors
C.One factor
D.Two factors
Correct Answer: Two factors
Explanation:
Parallel analysis retains observed eigenvalues exceeding their random-data counterparts. Only and satisfy that criterion.
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48After extracting a two-factor model, one variable has loadings and . Assuming standardized variables and no additional factor contributions, what are its communality and uniqueness?
Factor analysis procedure
Hard
A.Communality and uniqueness
B.Communality and uniqueness
C.Communality and uniqueness
D.Communality and uniqueness
Correct Answer: Communality and uniqueness
Explanation:
For orthogonal factors, communality is the sum of squared loadings: . Uniqueness is .
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49An unrotated orthogonal loading matrix is transformed as , where . Which quantity is necessarily unchanged for every observed variable?
Rotation in factor analysis
Hard
A.Its loading on the first retained factor
B.Its ranking by the largest absolute loading
C.Its communality across the retained factors
D.Its correlation with each individual factor
Correct Answer: Its communality across the retained factors
Explanation:
An orthogonal rotation preserves each row's squared length because . Thus communalities remain unchanged, although individual loadings can change.
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50In an oblique factor solution, variable has pattern coefficients , and the factor correlation matrix is . What is the structure coefficient of with the second factor?
Rotation in factor analysis
Hard
A.
B.
C.
D.
Correct Answer:
Explanation:
The structure matrix is . For the second factor, the coefficient is .
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51A researcher expects two substantive constructs to be correlated but applies varimax rotation. What is the most important interpretive risk?
Rotation in factor analysis
Hard
A.The resulting factors will no longer reproduce the correlation matrix
B.The rotation will necessarily change every variable's communality
C.The extraction will necessarily retain too many common factors
D.The solution may redistribute shared construct variance into cross-loadings
Correct Answer: The solution may redistribute shared construct variance into cross-loadings
Explanation:
Varimax constrains factors to be uncorrelated. If constructs are genuinely correlated, their shared association may appear as more complex loading patterns or cross-loadings.
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52A cluster analysis uses Euclidean distance on income measured in dollars and satisfaction measured on a seven-point scale. Income has far greater numerical variance. What is the most defensible preprocessing decision?
Overview of cluster analysis
Hard
A.Retain raw scales because clustering automatically adjusts variable variance
B.Convert Euclidean distances into squared correlations after clustering
C.Standardize observations within each cluster after estimating memberships
D.Standardize variables using substantively justified scales before clustering
Correct Answer: Standardize variables using substantively justified scales before clustering
Explanation:
Euclidean distance is scale-sensitive, so income would dominate. Pre-clustering standardization or domain-based scaling prevents units from determining the solution.
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53Under Ward's method with squared Euclidean distance, clusters and contain 4 and 6 cases, and their centroids are 5 units apart. What is the increase in within-cluster sum of squares if they are merged?
Overview of cluster analysis
Hard
A.
B.
C.
D.
Correct Answer:
Explanation:
Ward's merge cost is . Thus .
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54A hierarchical single-linkage solution joins two dense, well-separated groups through a sequence of sparse intermediate observations. Which diagnosis best explains the result?
Overview of cluster analysis
Hard
A.A rotation effect caused by correlated cluster axes
B.A centroid reversal caused by unequal cluster sizes
C.A suppression effect caused by negative communalities
D.A chaining effect caused by nearest-neighbor links
Correct Answer: A chaining effect caused by nearest-neighbor links
Explanation:
Single linkage merges clusters according to the closest pair of observations. Sparse bridges can therefore connect otherwise distinct dense groups, producing chaining.
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55Two groups have multivariate normal distributions with equal covariance matrices but unequal prior probabilities. Relative to equal priors, how does incorporating the unequal priors affect the linear discriminant boundary?
Discriminant analysis
Hard
A.It converts the linear boundary into a quadratic boundary
B.It changes the orientation while retaining the same boundary intercept
C.It shifts the intercept while retaining the same boundary orientation
D.It leaves both the orientation and intercept unchanged
Correct Answer: It shifts the intercept while retaining the same boundary orientation
Explanation:
With a common covariance matrix, the normal vector depends on the means and pooled covariance. Priors enter through a log-prior term, shifting the intercept without changing orientation.
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56In a three-group discriminant analysis with five predictors, what is the maximum number of nonzero canonical discriminant functions?
Discriminant analysis
Hard
A.Four functions
B.Three functions
C.Two functions
D.Five functions
Correct Answer: Two functions
Explanation:
The maximum number is . With predictors and groups, this is .
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57A study applies quadratic discriminant analysis with 80 predictors but only 25 observations in each group. What is the central estimation problem?
Discriminant analysis
Hard
A.The pooled covariance matrix must equal the identity matrix
B.Each group covariance matrix is singular or highly unstable
C.The discriminant functions cannot include group prior probabilities
D.The group means become identical after predictor standardization
Correct Answer: Each group covariance matrix is singular or highly unstable
Explanation:
Quadratic discriminant analysis estimates a separate covariance matrix for each group. With more predictors than within-group observations, these matrices cannot be reliably inverted without regularization.
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58A nonmetric multidimensional scaling solution has low stress, but its recovered distances are not linearly related to the original dissimilarities. Why can the solution still be valid?
Multidimensional scaling
Hard
A.Nonmetric MDS primarily preserves the rank order of dissimilarities
B.Nonmetric MDS primarily preserves the covariance of object coordinates
C.Nonmetric MDS constrains every disparity to equal its original value
D.Nonmetric MDS requires only equal average distances among all objects
Correct Answer: Nonmetric MDS primarily preserves the rank order of dissimilarities
Explanation:
Nonmetric MDS fits a monotonic transformation of dissimilarities. It seeks ordinal agreement, so a nonlinear relationship can still yield low stress.
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59In classical MDS, double-centering the squared dissimilarity matrix produces a matrix with several substantial negative eigenvalues. What is the best interpretation?
Multidimensional scaling
Hard
A.The dissimilarities are not exactly representable in Euclidean space
B.The configuration is Euclidean but requires only one dimension
C.The original dissimilarities must have been measured on a ratio scale
D.The object coordinates are unique up to translation but not rotation
Correct Answer: The dissimilarities are not exactly representable in Euclidean space
Explanation:
For exact Euclidean distances, the double-centered inner-product matrix is positive semidefinite. Substantial negative eigenvalues indicate non-Euclidean dissimilarities or incompatibility with an exact Euclidean representation.
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60An effects-coded conjoint model estimates level utilities for a three-level attribute as for level 1 and for level 2. What utility is implied for level 3?
Conjoint analysis
Hard
A.
B.
C.
D.
Correct Answer:
Explanation:
Effects coding imposes a zero-sum constraint within the attribute. Therefore, the third utility is .
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