Multivariate Supplement
Multiple Choice Questions
1. Statistical techniques that focus upon and bring out the structure of simultaneous
relationships among three or more variables are called _____ analysis.
A. bivariate
B. multivariate
C. parametric
D. nonparametric
E. ratio
,2. Which of the following statistical techniques is appropriate when the variables to be analyzed
are interrelated without designations as to whether they are criterion and predictor variables?
A. Multiple regression
B. Multivariate analysis of variance
C. Discriminant analysis
D. Factor analysis
E. Path analysis
3. All of the following types of techniques are useful for data reduction except _____.
A. factor analysis
B. multivariate analysis of variance
C. cluster analysis
D. multidimensional scaling
E. all are useful for data reduction
,4. Multidimensional scaling is a statistical technique that _____.
A. identifies homogeneous subgroups
B. identifies patterns underlying combinations of the original variables capable of
summarizing the original set
C. develops a perceptual map of the locations of some objects relative to others
D. predicts or explains the value for a dependent variable using the values of independent
variables
E. uses two or more independent, metric variables to classify observations into categories of
a nominal, dependent variable
5. Which of the following statistical techniques identifies homogenous subgroups?
A. Factor analysis
B. Multivariate analysis of variance
C. Cluster analysis
D. Multidimensional scaling
E. Discriminant analysis
, 6. Which of the following statistical techniques identifies patterns underlying combinations of the
original variables capable of summarizing the original set?
A. Factor analysis
B. Multivariate analysis of variance
C. Cluster analysis
D. Multidimensional scaling
E. Discriminant analysis
7. Which of the following statistical techniques predicts or explains the value for a dependent
variable using the values of independent variables?
A. Factor analysis
B. Multivariate analysis of variance
C. Multiple regression
D. Multidimensional scaling
E. Discriminant analysis
Multiple Choice Questions
1. Statistical techniques that focus upon and bring out the structure of simultaneous
relationships among three or more variables are called _____ analysis.
A. bivariate
B. multivariate
C. parametric
D. nonparametric
E. ratio
,2. Which of the following statistical techniques is appropriate when the variables to be analyzed
are interrelated without designations as to whether they are criterion and predictor variables?
A. Multiple regression
B. Multivariate analysis of variance
C. Discriminant analysis
D. Factor analysis
E. Path analysis
3. All of the following types of techniques are useful for data reduction except _____.
A. factor analysis
B. multivariate analysis of variance
C. cluster analysis
D. multidimensional scaling
E. all are useful for data reduction
,4. Multidimensional scaling is a statistical technique that _____.
A. identifies homogeneous subgroups
B. identifies patterns underlying combinations of the original variables capable of
summarizing the original set
C. develops a perceptual map of the locations of some objects relative to others
D. predicts or explains the value for a dependent variable using the values of independent
variables
E. uses two or more independent, metric variables to classify observations into categories of
a nominal, dependent variable
5. Which of the following statistical techniques identifies homogenous subgroups?
A. Factor analysis
B. Multivariate analysis of variance
C. Cluster analysis
D. Multidimensional scaling
E. Discriminant analysis
, 6. Which of the following statistical techniques identifies patterns underlying combinations of the
original variables capable of summarizing the original set?
A. Factor analysis
B. Multivariate analysis of variance
C. Cluster analysis
D. Multidimensional scaling
E. Discriminant analysis
7. Which of the following statistical techniques predicts or explains the value for a dependent
variable using the values of independent variables?
A. Factor analysis
B. Multivariate analysis of variance
C. Multiple regression
D. Multidimensional scaling
E. Discriminant analysis
