What are resampling techniques in statistics?
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What are resampling techniques in statistics?
Resampling techniques are methods used to repeatedly draw samples from a dataset to assess variability and improve estimates.
Can you explain the different types of resampling techniques?
Sure! Common types include bootstrapping, cross-validation, and permutation tests. Each serves different purposes.
How does bootstrapping work?
Bootstrapping involves repeatedly sampling with replacement from the original dataset to create 'new' samples for analysis.
What is cross-validation used for?
Cross-validation is used to evaluate the performance of a model by partitioning the data into subsets for training and testing.
Are there any limitations to these techniques?
Yes, limitations include potential overfitting in cross-validation and bias in bootstrapping if the original sample is not representative.
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