Many methods of Multi-Variable Analysis can be used in the real world today.
There are two types in these methods.
One is the model that deals with the complicated relations among variables. For example, SEM and Covariance Structure Analysis , Bayesian Network and Associations Analysis .
The other is the model that one Y variable and many X variables. For example, Support Vector Machine and Neural Network .
In classical methods, the analyst has to set the arrows.
But in Bayesian Network and Associations Analysis , the method find arrows automatically.
So these tools are strong as the method of Statistical Way of Making Hypothesis .
We need to understand that the arrows are find by Asymmetry of Quantity Data . And the methods do not find the rules of causes and effects.
The basic models deals with Linearity . But Support Vector Machine and Neural Network can deals with non-linearity.
The difficulty of complicated models have complicated difficulty of classical models.
So it is not easy to use these models.
The output of complicated models cannot be used as the solution directly.
I often use complicated models to get the hints to solve problems. I also use classical models and graphical approaches.
There are two types of complicated models. And maybe there is the hybrid model of the two. But we need to avoid various faults of analysis.
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