IBM SPSS Bootstrapping
IBM 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.
SPSS Bootstrapping Screenshots
The descriptives table contains statistics and bootstrap confidence intervals for those statistics.
The bootstrap confidence interval for the mean (86.39, 105.20) is similar to the parametric confidence interval (86.42, 105.30) and suggests that
the "typical" employee has roughly 7-9 years of previous experience. However, Previous Experience (months) has a skewed distribution, which makes
the mean a less desirable indicator of “typical” current salary than the median.
Confidence interval for proportions – Statistic column
The Statistic column shows the values usually produced by Frequencies, using the original dataset. The Bootstrap columns are produced by the bootstrapping algorithms.
- Bias is the difference between the average value of this statistic across the bootstrap samples and the value in the Statistic column.
In this case, the mean value of Churn within last month is computed for all 1000 bootstrap samples, and the average of these means is then computed.
- Std. Error is the standard error of the mean value of Churn within last month across the 1000 bootstrap samples.
- The lower bound of the 95% bootstrap confidence interval is an interpolation of the 25th and 26th mean values of Churn within last month,
if the 1000 bootstrap samples are sorted in ascending order. The upper bound is an interpolation of the 975th and 976th mean values.