Bootstrap

What Bootstrap is

Bootstrapping is a resampling technique used to estimate statistics on a population by sampling a dataset with replacement. It can be used to estimate the population mean, standard deviation, correlation, and other statistics.

Steps for bootstrapping:

1. Take a random sample from the population, with replacement.

2. Record the statistics from the sample.

3. Repeat steps 1 and 2 multiple times to create a sampling distribution.

4. Calculate the mean of the sampling distribution. This is the bootstrapped estimate of the population mean.

5. Calculate the standard deviation of the sampling distribution. This is the bootstrapped estimate of the population standard deviation.

6. Calculate the correlation coefficient of the sampling distribution. This is the bootstrapped estimate of the population correlation coefficient.

Examples

1. Bootstrap can be used to estimate the standard errors of estimated parameters in regression models.
2. Bootstrap can be used to evaluate the accuracy of a model by estimating its prediction error.
3. Bootstrap can be used to calculate confidence intervals for a statistic.
4. Bootstrap can be used to determine the statistical significance of sample differences between two or more samples.
5. Bootstrap can be used to assess model accuracy by estimating the bias and variance of a model.
6. Bootstrap can be used to compare the performance of different statistical models.
7. Bootstrap can be used to estimate the accuracy of a model by testing it on different subsets of the data.
8. Bootstrap can be used to estimate the standard error of a statistic based on bootstrapping the data.

Related Topics

• DEVIATION

• STATISTIC

• ACCURACY

• MEAN

• SIGNIFICANCE

• CORRELATION

• POPULATION

• VARIANCE

• REGRESSION

• SAMPLE

• BIAS

• RESAMPLING