Implement bootstrap mean and variance

You are given an array of `N` numeric observations `x[0..N-1]`. Write a function that uses bootstrap resampling to estimate: 1. the mean of the original sample, 2. the average of the bootstrap replicate means, and 3. the variance of the bootstrap replicate means. Assume the function also receives an integer `B`, the number of bootstrap replicates. For each replicate: - sample `N` values with replacement from the original array, - compute the replicate mean, - store that mean. Return at least these outputs: - `original_mean` - `bootstrap_mean` - `bootstrap_variance` Also explain how you would handle edge cases such as `N = 0`, `B <= 0`, repeated values, and numeric stability. State the expected time and space complexity.