DEPY (Decomposition of Explained and Unexplained Predictive Models) is a statistical method that helps in understanding the relationship between dependent and independent variables in a predictive model.

DEPY is a powerful tool that can identify the significant variables in a model and differentiate between their linear and non-linear contributions. It provides an understanding of which variables are responsible for the variations in the dependent variable.

Type 1 DEPY

Type 1 DEPY is a simple technique of decomposing the explained and unexplained variances in a dependent variable. This type of DEPY is used when you have a single dependent variable and a set of independent variables.

In Type 1 DEPY, the total variance of the dependent variable is divided into explained and unexplained variance. The explained variance is the variance that can be accounted for by the independent variables, while the unexplained variance is the variance that cannot be accounted for.

In other words, Type 1 DEPY helps you to understand how much of the variation in the dependent variable is explained by the independent variables in your model.

It can also help you to identify which independent variables are significant in explaining the variation in the dependent variable.

Type 2 DEPY

Type 2 DEPY is more complex compared to Type 1 DEPY. This type of DEPY is used when you have multiple dependent variables and a set of independent variables.

In Type 2 DEPY, the variance in multiple dependent variables is decomposed into explained and unexplained variance. The explained variance is the variance that can be accounted for by the independent variables, while the unexplained variance is the variance that cannot be accounted for.

Type 2 DEPY helps you identify which independent variables are significant in explaining the variation in each of the dependent variables.

This is useful when you have multiple dependent variables and want to understand the relationship between them and the independent variables.

Type 3 DEPY

Type 3 DEPY is the most complex type of DEPY. This type of DEPY is used when you have multiple dependent variables and multiple sets of independent variables.

In Type 3 DEPY, the variance in multiple dependent variables is decomposed into explained and unexplained variance for each set of independent variables.

Type 3 DEPY helps you to identify the contribution of each set of independent variables in explaining the variation in each of the dependent variables.

This is useful when you have multiple sets of independent variables, and you want to understand the relationship between them and the dependent variables.

Factors to Consider When Choosing the Type of DEPY

When choosing the type of DEPY to use, you should consider the following factors:.

The number of dependent variables you have
The number of independent variables you have
The relationship between the dependent and independent variables
The complexity of the model

Type 1 DEPY is simple and can easily be used when you have only one dependent variable.

If you have multiple dependent variables, you should use either Type 2 or Type 3 DEPY, depending on the relationship between the dependent and independent variables, and the complexity of the model.

Conclusion

DEPY is a statistical method that can help you understand the relationship between dependent and independent variables in a predictive model.

There are three types of DEPY that can be used depending on the number of dependent and independent variables, and the complexity of the model. Type 1 DEPY is used when you have a single dependent variable, and Type 2 and Type 3 DEPY are used when you have multiple dependent variables.

Each type of DEPY helps you to identify the significant independent variables and their contribution to the variation in the dependent variable.

When choosing the type of DEPY to use, you should consider the number of dependent and independent variables, the relationship between them, and the complexity of the model.