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MultiVariate Analysis


Also referred to as "Multiple Index Analysis" Tabular and graphical reports that cross reference multiple indices. Two dimensional and 33 dimensional matrices.
While each of
these bivariate correlations
reveals interesting insight into the portfolio, the owners and
decisions makers
benefit from multivariate analysis. For example:
Types of Multivariate Analysis Listed below are some of the different types of multivariate analysis:
Further discussion on each of these matrices can be found on the corresponding pages of this glossary.
Applications of Multivariate Analysis Through
interactions with elected local
government officials and their city staff, the authors have
participated in
dialogues on the challenges in making reasonable and defensible
decisions in the
public good, including:
These complex
decisions
require insight into the correlations between multiple variables, such
as: age,
condition, obsolescence, energy efficiency and mission criticality. The
correlation of two variables (say agecondition) returns a bivariate
analysis,
whereas the correlation of three or more variables (say,
ageconditioncriticality)
returns multivariate insight. 
Fig. Two key performance indicators (KPIs) confirming that a facility is in relatively "good" condition. Fig. 3D matrix to cross reference three separate indexes: #1 Facility Condition Index (FCI) with the #2 MissionDependency Index (MDI) and E3 backlog quantum. Fig. Multivariate analysis in the form of matrix overlays: conditionage matrix, conditionenergy matrix, and conditioncriticality matrix 

See also:

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