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In this analysis, an OLS regression was estimated with the same covariates used in the estimation of the propensity score, but with a different dependent variable, walking distance to the nearest hospital.
Thirdly, ZIP regression was estimated (Appendix 2).
Firstly, the standard Poisson regression was estimated (Appendix 1).
After that, the corresponding multilevel binary logistic regression was estimated.
The value of k (slope of regression) was estimated by liner regression between ln of E. coli concentration (CFU/ml) and time (day).
To allow for the possibility of income varying across various BMI levels, a quantile regression was estimated at the 25th, 50th and 75th percentiles for the whole sample and the results are presented in Table 4.
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We obtain the predicted value of ROE from a probit estimation (Table 5); the second stage probit regression is estimated using this predicted value of ROE.
The regression is estimated separately for women and men.
The regression is estimated separately for 1993 and 1997.
The first-stage regression is estimated using the full sample, which consists of 259 observations.
To handle the endogeneity problem in the models described above, a two-stage regression is estimated.
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