Bootstrapping is an efficient way to ensure that analytical models are reliable and will produce accurate results. It can be used to test the stability of analytical models and procedures found throughout the SPSS Statistics product family, including descriptive, means, crosstabs, correlations, regression and many others.
Bootstrapping is a method for deriving robust estimates of standard errors and confidence intervals for estimates such as the mean, median, proportion, odds ratio, correlation coefficient or regression coefficient. It may also be used for constructing hypothesis tests.
SPSS Bootstrapping enables you to:
Quickly and easily estimate the sampling distribution of an estimator by re-sampling with replacement from the original sample.
Create thousands of alternate versions of a data set for a more accurate view of what is likely to exist in the population.
Reduce the impact of outliers and anomalies, helping to ensure the stability and reliability of your models.
Estimate the standard errors and confidence intervals of a population parameter such as the mean, median, proportion, odds ratio, correlation coefficient, regression coefficient and more.