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Table 12 The best regression model found by the MCMC sampler for the EBL estimator

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

129.60

6.17

21.00

0.00

< .001

–

–

nb3s

1076.00

28.09

33.32

0.00

< .001

8.97

0.31

nb7v

− 1882.00

56.70

− 33.20

0.00

< .001

7.39

0.48

nb7c

61.42

4.95

12.39

0.00

< .001

1.25

0.15

ds15s

− 2112.00

109.10

− 19.36

0.00

< .001

2.17

0.22

ds35c

239.90

40.13

5.98

0.00

< .001

2.58

0.10

ch127

0.57

0.02

− 26.76

0.00

< .001

2.19

0.23

ch357

− 0.57

0.02

− 31.22

0.00

< .001

1.50

0.24

  1. This table summarizes the best regression equation returned by the MCMC sampler for the estimation of \(\sqrt{d} using the EBL estimator\). 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