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Asked by Ramya Last Modified
Answer Bootstrap resampling is a statistical technique used for estimating the sampling distribution of a statistic by repeatedly resampling with replacement from the observed data. The term "bootstrap" is derived from the phrase "pulling oneself up by one's bootstraps," suggesting the idea of self-sufficiency.
Here's an intuitive explanation:
Original Sample: Imagine you have a small dataset representing some population, and you want to make inferences about a particular statistic (mean, median, variance, etc.) calculated from that data.
Resampling: Instead of collecting new samples from the population, you repeatedly draw samples from your original dataset with replacement. This means that each time you draw an observation, you put it back before drawing the next one. As a result, some observations may be included more than once in a resampled dataset, while others may be left out.
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