# Resampled statistics

Resampling algorithms such as bootstrap or jackknife allow to approach the distribution of a statistic. Available in Excel using the XLSTAT software.

## What is resampling

Resampling methods such as Jackknife or Bootstrap have become more and more popular since computational power has increased. It is a well-known approach to nonparametric statistics. The principle is very simple: from your original sample, randomly draw a new sample and recalculate statistics. Repeating this step many times gives you the empirical distribution of the statistic, from which you obtain the standard error, and confidence intervals.

## Resampling in XLSTAT

With XLSTAT, you can apply these methods on a selected number of descriptive statistics for quantitative data.

Three resampling methods are available:

• Bootstrap: It is the most famous approach; it has been introduced by Efron and Tibisharni (1993). It is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample. The number of samples has to be given.
• Random without replacement: Subsamples are drawn randomly from the original sample. The size of the subsample has to be specified.
• Jackknife: The sampling procedure is based on suppressing one observation to the original sample (of size N). Each subsample has N-1 observations and the process is repeated N times. It is less robust than the bootstrap.

Although you can select several variables (or samples) at the same time, XLSTAT calculates all the descriptive statistics for each of the samples independently.

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