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Data that changes over time at some frequency, either annual, monthly, daily or some other interval of time

A quantity that can assume any of a set of values.
A symbol into which data can be stored. 

The weather is the most common example of a variable that can have an impact on other variables.

Physical Variables:
Listed below are some common examples of physical variables in the field of asset management.
Financial Variables:
Listed below are some common examples of financial variables in the field of asset management.
Variables are used to determined future states.
Factor analysis is a data reduction technique for identifying the internal structure of a set of variables. Unlike other techniques like Regression analysis or ANOVA, factor analysis does not require that predictor and criterion variables be defined. Factor analysis attempts to identify the relationship between all variables included in the analysis set.

Factor analysis is decompositional in nature in that it identifies the underlying relationships that exist within a set of variables. Factor analysis creates groups of metric variables (interval or ratio scaled) called factors. A factor is an underlying quality found to be characteristic of the original variables. Two types of factors exist. Common factors have effects shared in common with more than one observed variable. Unique factors have effects that are unique to a specific variable.

The basic objectives of a Factor Analysis are:

To determine how many factors are needed to explain the set of variables.
To find the extent to which each variable is associated with each of a set of common factors.
To provide interpretation to the common factors.
To determine the amount of each factor possessed by each observation. (Identified by the factor scores)
In summary then, the goal is to explain a portion of their variance in the set of variables input into the analysis by identifying certain underlying common dimensions called the factors.

See also: