3.4.1. Logistic Regression
Logistic Regression by [56] is a so-called generalized linear model
(GLM) and is one of the most widely used classifiers. According to
[57], when having a binary response as in this research, by using
Logistic Regression one typically aims at estimating the conditional
probability\(P\left(Y\ =\ 1\middle|X\ =\ x\right)\ =\ E[Y|X\ =\ x]\)where \(X\ =\ (X_{1},X_{2}\ ...,\ X_{p})\). Since logistic regression
falls under the category of GLM, the significance of each variable in
the classification process can be accurately identified. Table 3 shows
the result for Logistic regression with initial containment as the
dependent variable. Of all the independent variables, the availability
of beds in hospitals and the percentage of lockdown days significantly
and positively affected the signs of initial containment. Other
variables did not significantly influence the dependent variables. The
model had an accuracy of 78.57% in the classification.
Table 3. Logistic Regression Results