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Table 8 The best regression model found by the MCMC sampler

From: Estimating the size of urban populations using Landsat images: a case study of Bo, Sierra Leone, West Africa

(1) Covariates

(2) Estimated coefficient

(3) Std. Error

(4) t value

(5) Pr(\(>|t|\))

(6) Max. Prob.

(7) VIF

(8) PIP

(intercept)

− 174.02

20.16

− 8.63

9.63E−7

< .0001

–

–

nb7v

− 1656.91

85.58

− 19.36

5.72E−11

< .0001

6.29

0.9837

r_sp37

532.23

31.27

17.02

2.88E−10

< .0001

1.34

0.9790

nb1v

1686.14

71.28

23.66

4.52E−12

< .0001

5.36

0.9500

r_sp15s

− 2744.29

159.21

− 17.24

2.46E−10

< .0001

1.08

0.4711

ch245c

44775.32

2636.73

16.98

2.96E−10

< .0001

2.72

0.9835

r_sp14c

− 246.75

23.79

− 10.37

1.18E−07

< .0001

1.55

0.5381

  1. This table summarizes the best regression equation returned by the MCMC sampler for the estimation of \(\sqrt{d}\). The values of the variance inflation factor (VIF) are less than 7.0, which demonstrates the low collinearity between the covariates. Four of Posterior Inclusion Probabilities (PIPs) are close to 1.0, quantifying their importance as predictive variables of \(\sqrt{d}\), as discussed in the text
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International Journal of Health Geographics

ISSN: 1476-072X