ABSTRACT
The term bootstrapping originates in the expression of “pulling oneself up by their bootstraps,” meaning to “succeed only by one’s own efforts or abilities.” From a statistical perspective, bootstrapping alludes to succeeding in being able to study the effects of sampling variation on estimates from the “effort” of a single sample. Or more precisely, it refers to constructing an approximation to the sampling distribution using only one sample. The infer package is an R package for statistical inference. The visualize() verb provides a quick way to visualize the bootstrap distribution as a histogram of the numerical stat variable’s values. The workaround to having a single sample was to perform bootstrap resampling with replacement from the single sample. Using these theory-based standard errors, let’s present a theory-based method for constructing 95% confidence intervals that does not involve using a computer, but rather mathematical formulas.
