pastas.solver.least_squares.LeastSquaresBase.ci_block_response
==============================================================

.. py:method:: pastas.solver.least_squares.LeastSquaresBase.ci_block_response(name: str, n: int = 1000, alpha: float = 0.05, max_iter: int = 10, **kwargs) -> pandas.DataFrame

   
   Calculate the confidence interval for the block response.

   :param name: Name of the block response for which to calculate the confidence interval.
   :type name: str
   :param n: Number of random samples drawn from the bivariate normal distribution to
             compute the confidence interval. Default is 1000.
   :type n: int, optional
   :param alpha: Significance level for the confidence interval. Default is 0.05, which
                 corresponds to a 95% confidence interval.
   :type alpha: float, optional
   :param max_iter: Maximum number of iterations for truncated multivariate sampling, default
                    is 10. Increase this value if number of accepted parameter samples is
                    lower than n.
   :type max_iter: int, optional
   :param \*\*kwargs: Additional keyword arguments are passed to the `ml.get_block_response()` method.

   :returns: * **data** (*Pandas.DataFrame*) -- DataFrame of length number of observations and two columns labeled
               0.025 and 0.975 (numerical values) containing the 2.5% and 97.5%
               interval (for alpha=0.05)
             * *\*\*kwargs* -- Additional keyword arguments are passed to the `ml.get_block_response()`
               method.

   .. rubric:: Notes

   The confidence interval shows the uncertainty in the simulation due
   to parameter uncertainty. In other words, there is a 95% probability
   that the true best-fit line for the observed data lies within the
   95% confidence interval.















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