A histogram divides sample values into many intervals and represents the frequency of data values in each interval with a bar.
The bar chart shows the proportion of occurrences for each category.
Minitab displays a bar chart when you take only one resample. Minitab displays both the original data and the resample data.
The sample size (N) is the total number of observations in the original sample. Minitab takes resamples of this sample size to form the bootstrap samples.
The sample proportion equals the number of events divided by the sample size (N).
Minitab displays two different proportion values, the proportion of the observed sample and the proportion of the bootstrap distribution (Average). Both these values are an estimate of the population proportion and will usually be similar. If there is a large difference between these two values, you should increase the sample size of your original sample.
Because the proportion is based on sample data and not on the entire population, it is unlikely that the sample proportion equals the population proportion. To better estimate the population proportion, use the confidence interval.
The number of resamples is the number of times Minitab takes a random sample with replacement from your original data set. Usually, a large number of resamples works best. The sample size for each resample is equal to the sample size of the original data set. The number of resamples equals the number of observations on the histogram.
The average is the sum of the proportions in the bootstrapping sample divided by the number of resamples.
Minitab displays two different proportion values, the proportion of the observed sample and the proportion of the bootstrap distribution (Average). Both these values are an estimate of the population proportion and will usually be similar. If there is a large difference between these two values, you should increase the sample size of your original sample.
Because the proportion is based on sample data and not on the entire population, it is unlikely that the sample proportion equals the population proportion. To better estimate the population proportion, use the confidence interval.
Confidence intervals are based on the sampling distribution of a statistic. If a statistic has no bias as an estimator of a parameter, its sampling distribution is centered at the true value of the parameter. A bootstrapping distribution approximates the sampling distribution of the statistic. Therefore, the middle 95% of values from the bootstrapping distribution provide a 95% confidence interval for the parameter. The confidence interval helps you assess the practical significance of your estimate for the population parameter. Use your specialized knowledge to determine whether the confidence interval includes values that have practical significance for your situation.
Minitab does not calculate the confidence interval when the number of resamples is too small to obtain an accurate confidence interval.
 

In these results, the estimate for the population proportion is approximately 0.62. You can be 95% confident that the population proportion is between approximately 0.56 and 0.69.