Skip to main content

Table 2 Classical regression model, considering as dependent variable the average detection rate by census tract

From: Spatio-temporal analysis of leprosy risks in a municipality in the state of Mato Grosso-Brazilian Amazon: results from the leprosy post-exposure prophylaxis program in Brazil

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

  1. 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)
Back to article page

Infectious Diseases of Poverty

ISSN: 2049-9957

Contact us