IBM SPSS Bootstrapping
Create More Reliable Models and Generate More Accurate Results
- Overview
- Features and Benefits
IBM® SPSS® Bootstrapping makes it simple to test the stability and reliability of your models so that they produce accurate, reliable results.
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.
IBM SPSS Bootstrapping provides an efficient way to ensure that your models are stable and reliable, so your analysis generates more accurate results. 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 dataset for more accurate analysis
IBM SPSS Bootstrapping helps reduce the impact of outliers and anomalies that can degrade the accuracy or applicability of your analysis. As a result, you have a clearer view of your data for creating the model you are working with.
- Fast, easy re-sampling -- estimate the sampling distribution of an estimator in a snap
- Reduce the impact of outliers and anomalies -- ensure the stability and reliability of your models
- Bootstrap many analytical procedures -- test a wide range of the descriptive and modeling procedures found in the IBM SPSS Statistics product family
IBM SPSS Bootstrapping works with a number of analytical procedures in the IBM SPSS Statistics product family, including:
| Descriptive Procedures | Product |
| Descriptives | IBM SPSS Statistics Base |
| Frequencies | IBM SPSS Statistics Base |
| Examine | IBM SPSS Statistics Base |
| Means | IBM SPSS Statistics Base |
| Crosstabs | IBM SPSS Statistics Base |
| t tests | IBM SPSS Statistics Base |
| Correlations/Nonparametric correlations | IBM SPSS Statistics Base |
| Partial Correlations | IBM SPSS Statistics Base |
| Modeling Procedures | Product |
| One-way | IBM SPSS Statistics Base |
| UniAnova | IBM SPSS Statistics Base |
| Linear Regression | IBM SPSS Statistics Base |
| Discriminant | IBM SPSS Statistics Base |
| General Linear Models | IBM SPSS Advanced Statistics |
| Generalized Linear Models | IBM SPSS Advanced Statistics |
| Linear Mixed Models | IBM SPSS Advanced Statistics |
| Cox Regression | IBM SPSS Advanced Statistics |
| Nominal Regression | IBM SPSS Regression |
| Logistic Regression, Binary, Multinomial | IBM SPSS Regression |
| Logistic and Ordinal Regression | IBM SPSS Regression |