What it can do for your business

Bootstrapping is a useful technique for testing model stability. IBM SPSS Bootstrapping helps make it effective and easy. This module of IBM SPSS Statistics estimates the sampling distribution of an estimator by resampling with replacement from the original sample. You can estimate the standard errors and confidence intervals of a population parameter such as a mean, median, proportion, odds ratio, correlation coefficient, regression coefficient or others. Control the numbers of bootstrap samples, set a random number seed and indicate whether a simple or stratified method is appropriate.

Accelerate estimates

Quickly and easily estimate the sampling distribution of an estimator by resampling with replacement from the original sample.

Improve accuracy

Create thousands of alternate versions of a data set for a more accurate view of what is likely to exist in the population.

Ensure stability, reliability of models

Mitigate outliers and anomalies that can degrade the accuracy or applicability of your analysis. Gain a more comprehensive view of data for creating models.

Key features

  • Estimate standard errors and confidence intervals
  • Test stability of analytical models, procedures across SPSS
  • Easily control the number of bootstrap samples
  • Gain a more complete view of your data

Security and privacy in the cloud

  • IBM enables companies to scale and adapt quickly to changing business needs without compromising security, privacy or risk levels when using IBM cloud offerings.

    Learn more about IBM Cloud security

Product images

Frequencies variable selection
Frequencies variable selection
Frequencies Statistics settings
Frequencies Statistics settings
Bootstrap settings
Bootstrap settings