IBM SPSS BOOTSTRAPPING

Estimate the sampling distribution of an estimator by resampling
with replacement from the original sample

IBM SPSS BOOTSTRAPPING

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.

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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.

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IBM SPSS Bootstrapping Demo

In this brief demo, you'll see how to obtain confidence intervals for proportions using an example from the telecommunications industry.

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Ensure the stability of your models

This brochure describes how you can use IBM SPSS Bootstrapping to test the stability and reliability of your models so that they produce accurate, reliable results.

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Features spotlight

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

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