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What is the main purpose of resampling methods in statistics?
What is the main purpose of resampling methods in statistics?
- To reduce the computational requirements of fitting statistical models
- To decrease the variability of a linear regression fit
- To fit a model of interest once on the training set
- To draw samples from a training set and refit a model to obtain additional information (correct)
Why can resampling approaches be computationally expensive?
Why can resampling approaches be computationally expensive?
- Because they use the original training sample multiple times
- Because they involve fitting the same statistical method multiple times using different subsets of the training data (correct)
- Due to recent advances in computing power
- Because they involve fitting the statistical method only once
What type of information can be obtained from resampling methods that would not be available from fitting the model only once using the original training sample?
What type of information can be obtained from resampling methods that would not be available from fitting the model only once using the original training sample?
- Information about the mean of the data points
- Information about outliers in the data
- Information about the intercept of a linear regression model
- Information about the variability of the fitted model (correct)
Why are cross-validation and bootstrap considered important tools in statistical learning procedures?
Why are cross-validation and bootstrap considered important tools in statistical learning procedures?
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Which recent advances have made the computational requirements of resampling methods generally not prohibitive?
Which recent advances have made the computational requirements of resampling methods generally not prohibitive?
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In what scenario would resampling methods like cross-validation be useful?
In what scenario would resampling methods like cross-validation be useful?
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