Model

\[ \text{waste\_recycled} = \beta_0 + \beta_1 \times \text{waste\_incinerated} + \beta_2 \times \text{waste\_mismanaged} + \beta_3 \times \text{waste\_landfilled} + \epsilon \]

 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).