Bootstrap and Jackknife: Resampling Statistics in Python

This project explores statistical resampling using Bootstrap and Jackknife methods to estimate the mean, median, and variance, while evaluating the stability of these estimates with Confidence Intervals (CI) and Mean Squared Error (MSE). Bootstrap is more suitable for large datasets, as it provides smooth distribution estimates. On the other hand, Jackknife is limited to smaller datasets but remains useful for variance estimation and outlier detection. This project can be expanded to include real datasets or additional methods such as Bayesian Bootstrap or Cross-Validation.. Methods Used: Bootstrap: Performs resampling with replacement. Calculates statistical distributions (mean, median, and variance) based on 1000 bootstrap samples. Used to measure statistical uncertainty (variability). Jackknife: Removes one observation per iteration to create a new sample. Suitable for small datasets. Provides insight into the stability of the estimator when the data changes slightly.