Multivariate Data Analysis Questions
and Answers Grade A+ 2023
Types of Multivariate Techniques - -1. Principal components and common factor analysis
2. Multiple regression and Multiple correlation
3. Multiple discriminant analysis and logistic regression
4. Canonical correlation analysis
5. Multivaiate analysis of variance covariance
6. Conjoint analysis
7. Cluster analysis
8. Perceptual mapping, AKA Multidimentional scaling
9. Correspondence Analysis
10. Structural equation modeling and confirmatory factor analysis
-Factor analysis - -Analyzing interrelationships among large number of variables and to explain
those variables in terms of their common underlying dimensions (factor)
Objective: condensing information into a smaller set with minimal loss of information
-Multiple regression (MR) - -Single metric DV or IV related to 2+ metric IVs
Objective: predict the changes in DV in response to changes in IVs
Most achieved through "least squares"
Ex: DV=sales. IVs: adv. expense, #of salespeople, and # of stores
-(Multiple) Discriminant analysis (MDA or DA) and Logistic regression - -MDA- when DV is
nonmetric (dichotomous - male vs female OR mulichotomous - high, medium, & low) and IVs
are metric.
MDA Objective- understand group differences and be able to predict belonging to a certain
group based on IVs.
Logistic regression- combining MDA and multiple regression.
Objective: to David into groups, then analyze DV on IVs within groups. Can be used to compare
groups to show differences in predicting the groups.
-Canonical correlation - -Multiple metric DVs with multiple metric IVs.
Objective: to develop a linear combination of each set of variables (both IVs and DVs) in a
manner that maximizes the correlation between the two sets.
and Answers Grade A+ 2023
Types of Multivariate Techniques - -1. Principal components and common factor analysis
2. Multiple regression and Multiple correlation
3. Multiple discriminant analysis and logistic regression
4. Canonical correlation analysis
5. Multivaiate analysis of variance covariance
6. Conjoint analysis
7. Cluster analysis
8. Perceptual mapping, AKA Multidimentional scaling
9. Correspondence Analysis
10. Structural equation modeling and confirmatory factor analysis
-Factor analysis - -Analyzing interrelationships among large number of variables and to explain
those variables in terms of their common underlying dimensions (factor)
Objective: condensing information into a smaller set with minimal loss of information
-Multiple regression (MR) - -Single metric DV or IV related to 2+ metric IVs
Objective: predict the changes in DV in response to changes in IVs
Most achieved through "least squares"
Ex: DV=sales. IVs: adv. expense, #of salespeople, and # of stores
-(Multiple) Discriminant analysis (MDA or DA) and Logistic regression - -MDA- when DV is
nonmetric (dichotomous - male vs female OR mulichotomous - high, medium, & low) and IVs
are metric.
MDA Objective- understand group differences and be able to predict belonging to a certain
group based on IVs.
Logistic regression- combining MDA and multiple regression.
Objective: to David into groups, then analyze DV on IVs within groups. Can be used to compare
groups to show differences in predicting the groups.
-Canonical correlation - -Multiple metric DVs with multiple metric IVs.
Objective: to develop a linear combination of each set of variables (both IVs and DVs) in a
manner that maximizes the correlation between the two sets.
