Asked by Braeden Moody on May 15, 2024

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Bootstrapping is useful because it adds more values to the original data set.

A) True.You are able to add as many new values as needed to the original data set.
B) True.You are able to add a finite amount of new values to the original data set.
C) False.No new values are added to the data set.
D) False.Bootstrapping is only used to change the mean of the original data set.

Bootstrapping

A statistical method that involves resampling with replacement from a dataset to estimate the distribution of a statistic.

Original Data Set

The initial collection of data acquired before any manipulation, cleaning, or analysis has been conducted.

New Values

Refers to data points that have been introduced into a dataset or calculation, differing from previous values.

  • Acquire knowledge on the use of bootstrapping for resampling purposes to infer the distribution pattern of a statistic obtained from a sample.
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EM
Edgar MorenoMay 20, 2024
Final Answer :
C
Explanation :
Bootstrapping is a statistical method that resamples the original dataset with replacement to create many simulated samples. It does not add new values to the dataset; instead, it helps in estimating the distribution of a statistic (e.g., mean, variance) by sampling from the existing data.