Skip to main content

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.