Table 2.
Classical regression model, considering as dependent variable the average detection rate by census tract
| Variable | Test | GL | Coefficient | Probability |
|---|---|---|---|---|
| Classical regression | ||||
| Constant | 13.0747 | 0.0000 | ||
| Poverty | 3.3368 | 0.0109 | ||
| Water and garbage | 2.0661 | 0.1069 | ||
| Regression diagnostics | ||||
| Multicollinearity | Conditional number | 1.0152 | ||
| Normality of residuals | Jarque–Bera | 2 | 5.9743 | 0.0504 |
| Heteroscedasticity | Breuch-Pagan | 2 | 0.1901 | 0.9093 |
| Koenter-Basset | 2 | 0.1633 | 0.9216 | |
| White’s Robust | 5 | 6.2055 | 0.2867 | |
| Spatial dependence diagnostics | ||||
| ML (lag) | 1 | 0.0679 | 0.7945 | |
| ML robust (lag) | 1 | 0.0005 | 0.9822 | |
| ML (error) | 1 | 0.0708 | 0.7902 | |
| ML robust (error) | 1 | 0.0034 | 0.9535 | |
GL: Degrees of freedom, ML (lag): Lagrange Multipliers (Spatial Lag Model), ML robust (lag): Lagrange Multipliers robust (Spatial Lag Model), ML (error): Lagrange Multipliers (Spatial Error Model), ML robust (error): Lagrange Multipliers robust (Spatial Error Model)