Imagine having analysing a matter where you think there are multiple variables that are dependent onto regressor. However, you cannot put a finger on it so you can solve the coefficients of the other variables and understand the trends, impacts, and correlations among variables. So far above sentence may look like a complicated one; nonetheless, this is an everyday struggle of a statistician. Not a single matter even the simplest one cannot be one sided in the world of statistics; thus, the multiple factored; that is, regressions with more than one explanatory variable are everywhere. In fact, they are so common that their existence shows a good sign of an explanation for the topic of research.

Without going into technical details, we can say that single factors
are not common things to see in statistical reasoning as adding additional
explanatory variables to model gives more detailed versions of the explanation.
Thus, whether doing an analytical research or a predictive modelling, the
**multiple
factored** analysis is the best start. While the main source of information
stays the
same for the model, with every variable added the *significance* of the other
variables will increase; hence, *would form a strong reliable model.*

The latest system of thinking shows that analysis conducted confirms
whether the signs of a **predictive one, descriptive on, or a prescriptive one.**
Thus,
those three’s analytical background must demonstrate the variables and factors
available. That is, the factors added to model must have the signs of above three
types of analysis. For instance, while performing a predictive analysis, adding
single variable to your model may result in an outcome that you want to see;
however, it is certain that said outcome is not the one you need. In other words,
single variable model outcomes are spurious in general. In order to overcome this
spurious situation, additional variables must be added to model. Therefore, the
ambiguous effects that accepted as error before would become obvious since their
coefficients can be estimated in explanatory side of the model.

Therefore, the **multiple factored** analysis shows its reliability
in
every type of analytical research. What is more is that predictive tools’
probability of commuting type I and type II errors diminishes to minimal levels
where coefficients of the prediction model enjoys the high significance in terms of
both jointly and individually. Having a high overall significance strengthens your
position while performing a probability test or a descriptive test where you are
looking for a certain output to be outcome in a confident way. Moreover, the idea
behind the multiple factor addition helps you to find if there are any correlations
between variables, where the outcome may be more then helpful to solve some future
problems in **predicting and describing.**

Enhencer’s ability to do structured analysis with ** multiple
factors** is
unique in many ways. In fact, addition to all above benefits the Enhencer allow
users to put a strong multi-factored explanation to every model created. That is,
end-user is free to but not limited to add additional variables to models created
via Enhencer where the prediction analysis is made within seconds with high
significance levels. On the other hand, the

*storytelling ability of Enhencer*completes the user’s desired I/O algorithms; thus, evaluations with multiple factors become the cornerstone of modelling, unlike other systems.

In conclusion, the multi-variable explanatory modelling is now
becoming an industry standard on account of ** Enhencer.** Having put every
possible
variable to a model; hence, constructing an all-inclusive analysis is vital since
the prediction procedure requires comprehensiveness among all other variables.
Therefore, in order to have an all-inclusive and result oriented modelling users
must choose modelling tools that allows to add multiple factors into single model.