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Bootstrapping is a procedure for estimating the distribution of an estimator by resampling data or a model. Learn the history, approach, advantages, disadvantages and recommendations of bootstrapping in statistics.
Bootstrap aggregating, or bagging, is a technique that improves the stability and accuracy of machine learning algorithms by generating multiple models from bootstrap samples and averaging or voting their outputs. It is often applied to decision tree methods, but can be used with any type of method.
Learn about different methods of creating new samples based on one observed sample, such as permutation tests, bootstrapping, cross-validation, jackknife and subsampling. Compare their advantages, disadvantages and applications in various fields of statistics.
Learn about the definition, formula, interpretation and properties of the Pearson correlation coefficient, a measure of linear correlation between two variables. See examples, scatter diagrams and related concepts such as covariance, regression and decorrelation.
Bootstrap is a free and open-source library that simplifies the creation of responsive, mobile-first web pages with HTML, CSS and JS. It provides design templates, components and utilities for typography, forms, navigation, and more.
A confidence interval is an interval that is expected to contain a parameter being estimated with a certain probability. Learn how to construct and interpret confidence intervals for different parameters and distributions, and how they relate to hypothesis testing and bootstrapping.
Learn about stratified sampling, a method of sampling from a population that can be partitioned into subpopulations. Find out the advantages, disadvantages, strategies and formulas of this technique in statistics and computational statistics.
Learn how to choose the number of observations or replicates in a statistical sample based on various factors, such as confidence level, margin of error, and variability. Find formulas and examples for estimating proportions, means, and variances.