bootstrapmeetodeid
Bootstrapmeetodeid refers to a class of statistical methods that use resampling to estimate the sampling distribution of a statistic. The core idea behind bootstrap methods is to treat the observed sample as if it were the entire population. By repeatedly drawing samples with replacement from this original sample, one can simulate the process of drawing multiple samples from the true population.
These resampled datasets, known as bootstrap samples, are then used to calculate the statistic of interest
Bootstrap methods are particularly useful when analytical solutions are difficult or impossible to obtain, or when