*Logistic Regression: Risk Question for Disabled People DOI: http://dx.doi.org/10.5772/intechopen.106212*

#### **Table 2.**

*Point and interval estimates of the logistic model parameters considering visual disability as the response variable.*


#### **Table 3.**

*Point and interval estimates of the parameters of the logistic model considering hearing disability as the response variable.*




#### **Table 4.**

*Point and interval estimates of the parameters of the logistic model considering physical disability as response variable.*

In intellectual ability, the following variables were selected: *Identification*: region, sex, age, race, and birthplace; *Education*: reading and writing, day care, other graduation, and education; *Family*: union nature, marital status, and number of children; *Work*: income, time, return, condition, situation, and secondary work; and finally, *Joint model*: gender, age, birthplace, reading and writing, and education. For model selection, we get AIC = 14,548. BIC = 14,711, and DIC = 14,515.

Making a comparative study between the models given in **Tables 1**–**5**, we noticed that the model that included a smaller number of variables was the logistic model adjusted for intellectual disability, while the model that required the largest number of independent variables was for the number of deficiencies.

The adjustment by stereotype ordinal logistic regression was compared with binary logistic regression [1] and multinomial logistic regression [23], and visual, hearing, physical, intellectual, and multiple disabilities were considered.

It was found that, for all the different disabilities, the one that had the highest number of independent variables considered significant was for the regression methodology, binary logistic followed by the stereotype ordinal logistic regression methodology, and this can be motivated by the following facts:

To enable the use of dummy variables, the response variable had to be transformed to determine whether or not it has a disability, which increased the sensitivity of the analysis, making differences more easily detected.

The stereotype logistic regression methodology performed better in relation to the multinomial logistic regression methodology, as it took into account that the response categories were ordinal, contrary to what happened when the multinomial logistic



#### **Table 5.**

*Point and interval estimates of the parameters of the logistic model considering as the answer variable intellectual disability.*

regression model was applied, and this probably caused that the multinomial logistic regression methodology has little sensitivity and presents a smaller number of selected variables in the composition of its models [24].

Among the advantages of using multinomial logistic regression, we can mention the fact of not making assumptions about the probabilistic behavior of the independent variables, possibility of testing the significance of a large number of independent variables, and, finally, possibility of direct estimation of the probability of an observation belonging to a certain class [25, 26].
