*Prediction Analysis Based on Logistic Regression Modelling DOI: http://dx.doi.org/10.5772/intechopen.103090*

**Figure 4.** *Prediction chart showing the trends for wind force and percentage of generators according to the prediction model, for cases with no human cause.*

position, four are correctly classified (40% of the total). There are 23 + 4 = 27 cases out of 33 that are correctly classified, representing 66.7% of the studied incidents.

In Step 2, when the variable percentage of generators is included in the equation, for the incidents not having excursion, the number of correctly-classified cases has become 20, representing 87% of the total. There is an improvement in the number of correctly classified cases for the excursion cases, which are now 5 (50% of the total). In this second step, there are now 25 cases correctly classified, which means 75.8% of the studied incidents. Although, there has been an evident downgrade in the prediction of the model with the addition of the variable percentage of generators, the prediction for the cases with loss of position has improved.

In **Figure 4**, it can be seen how the model predicts a loss of position for more significant values of wind force and of the percentage of generators.

The relative ratio can show the prediction for loss of position for the different main causes, as shown in **Figure 5**. The dashed line allows us to appreciate better those mean values above 1, which show a higher likelihood of having a loss of position. The main causes that are more prone to end in an excursion, according to this new model, are environmental and references.

#### *5.3.2 Human cause*

The 9 cases where there is no human cause are selected.

#### **Figure 5.**

*Mean relative ratio for each main cause group, for the model obtained for the incidents without human cause, obtaining the likelihood of a loss of position.*


#### **Table 9.**

*Individual results in step 1 for each independent variable when the forward (Wald) binary regression model is performed, being excursion the dependent variable and selection variable human cause = 1 (human cause).*


**Table 10.**

*Variables in the equation in Step 1. It can be observed that the independent variable, with a p-value of 0.34, cannot be considered to have any relation with the loss of position or not.*

In the preliminary stage, the variables are introduced in the model one by one to check their significance for explaining the answer. The variables are presented in **Table 9**.

Considering these results, only the variable water depth is selected to enter the model.

In step 0, when the variable is not yet in the equation, it is considered significative (score 5.248, p-value 0.022), so it enters the equation in step 1. The different statistics obtained in Step 1 can be observed in **Table 10**.

It can be observed that the p-value associated with the Wald statistic is bigger than 0.1, which means that this variable does not explain the model with the desired significance, and so it must be rejected.

Out of the nine valid cases, six were not ending in an excursion, while three were losing position.

In Step 1, after the variable water-depth was included in the equation, it was obtained that from the 6 cases without excursion, according to the model, there were 5 cases correctly classified (83.3% of the total) and that from the ones having a loss of position, two are correctly classified (66.7% of the total). There are 5 + 2 = 7 cases out of 9 that are correctly classified, representing 77.8% of the studied incidents.
