Multivariate analytic techniques: Dependence vs. Interdependence
Dependence and interdependence are phrases used to describe different sorts of connections that exist inside the data. For a quick explanation:
Dependency strategies
Dependence approaches are employed when a number of the factors are interdependent. Dependence investigates whether there is a relationship between cause and effect, or more precisely, whether the outcomes of two or more independent factors can be used to describe, explain, or predict the outcomes of another predictor variables. For a simple example, let's say that the dependent variable "weight" can be predicted by the independent factors "height" and "age."
Machine learning uses dependencies approaches to build predictive models. The analyst defines which variables are separate and which one are dependent when entering input data into the model. In other words, the researcher describes the variables the model should utilise and the variables it should predict.
Interdependence techniques
Interdependence approaches are necessary to comprehend a dataset's structural composition and underlying patterns. Since no variables in this scenario are linked, causal linkages are irrelevant. Interdependence approaches, on the other hand, seek to assign a meaning to a set of parameters or to group factors together in meaningful ways.
As a result, one is interested in how various variables interact, whereas the other is solely concerned with the dataset's structure.
In light of this, let's consider some useful multivariate analysis techniques. We'll think about:
- there are several linear regression
- a handful of multiple variable logistic regression variance analyses (MANOVA)
- factor analysis
- Cluster analysis