# IBM SPSS Bootstrapping

Whether you conduct academic or scientific research, study issues in the public sector or provide the analyses that support business decisions, it’s important that your models are stable. Test model stability quickly and easily with IBM SPSS Bootstrapping. With IBM SPSS Bootstrapping, you can:

- Quickly and easily estimate the sampling distribution of an estimator by re-sampling with replacement from the original sample
- Estimate the standard errors and confidence intervals of a population parameter such as the mean, median, proportion, odds ratio, correlation coefficient, regression coefficient, and numerous others
- Create thousands of alternate versions of your datasets for more accurate analysis
- Operating systems supported: Windows, Mac, Linux

The IBM SPSS Bootstrapping module allows users to derive more robust estimates of a host of statistical values. These values may include means, medians, standard errors and confidence intervals as well as correlations and regression coefficients. Bootstrapping is useful in a number of situations including hypothesis testing as it provides an alternative to classical parametric estimation when the underlying assumptions of such methods are in danger of being violated such as when the error values in a linear regression solution are found to increase linearly.

Bootstrapping is also useful when inferential calculations require extremely complex formulas for calculating standard errors. This is especially true when computing standard errors in order to derive confidence intervals for median and percentile values. Specifically, Bootstrapping refers to the method of repeatedly resampling subsets of a data file (with replacement) and examining the variation in the resulting calculation of key statistics (such as a mean). By creating an internal sampling distribution of the statistic in question the technique allows for a more realistic estimate of that statistic’s parameter value.

For example, a digital retailer adds about 5% to its growing customer base every quarter. The management are interested to know if this figure hold true for customers across three separate age groups. By applying IBM SPSS Bootstrapping, analysts can figure out more accurately the degree to which the customer growth rate varies by age group.