Family: gaussian
Links: mu = identity; sigma = identity
Formula: waste_recycled ~ waste_incinerated + waste_mismanaged + waste_landfilled
Data: data (Number of observations: 200)
Draws: 4 chains, each with iter = 2000; warmup = 500; thin = 1;
total post-warmup draws = 6000
Regression Coefficients:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 100.00 0.00 100.00 100.00 1.02 612 263
waste_incinerated -1.00 0.00 -1.00 -1.00 1.02 650 265
waste_mismanaged -1.00 0.00 -1.00 -1.00 1.03 617 216
waste_landfilled -1.00 0.00 -1.00 -1.00 1.02 610 262
Further Distributional Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 0.00 0.00 0.00 0.00 1.67 6 35
Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
Model
\[ \text{waste\_recycled} = \beta_0 + \beta_1 \times \text{waste\_incinerated} + \beta_2 \times \text{waste\_mismanaged} + \beta_3 \times \text{waste\_landfilled} + \epsilon \]