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