Factor Analysis

In general, a factor is defined as an element contributing to a result. Factor Analysis is a method to derive new variable factors that relate to a set of sampled variables.

Processes, Inputs, and Outputs

There are a number of processes, inputs, and outputs surrounding Factor Analysis. In total, they can be seen as an iterative loop of refinement to achieve optimal Factor selection.

Items

Items come from a group of data under consideration. Examples of types of Items include:

  • customers of a business

  • buyers of a product

  • voters in an election

  • patients with an illness

Variable Selection

Variable Selection is the process of determining which Variables are pertinent to training and using a given Machine Learning model.

Variables

Variables are aspects of items. Examples include:

  • age

  • income

  • location

  • family size

Factor Analysis

Factor Analysis is the process of deriving new variable factors that relate to a set of sampled Variables. An example of this process is Principal Component Analysis.

Factors

Factors are measures derived from Variables. Examples include:

  • averages

  • groups (such as using income ranges instead of exact numbers)

  • ranges (such as date ranges instead of exact dates)

Variable Analysis

Variable Analysis is the process of measuring the Variance related to variables.

Variance Measure

Variance is a measure of the Deviation of variables around a mean value.

The Variance measure can be fed back into Variable Selection and Factor Analysis for iterative rounds of refining result.

References