In order to solve this question, we need to follow a few steps.
1. First, we generate 1.1 million numbers following a standard exponential distribution. Let's denote this collection of numbers as y. The exponential distribution is often used to model the time elapsed between events that occur continuously and independently at a constant average rate.
2. Next, we calculate the mean (or average) of the numbers in y. The mean is found by adding up all the numbers and then dividing by the quantity of numbers. The formula for calculating the mean of a set of numbers is Σx/n, where Σx is the sum of all the numbers and n is the count of numbers. In our case, the calculated mean of y is approximately 0.9999921494591629.
3. The last step is to find the standard deviation of y. Standard deviation is a measure of how much variance there is in a set of numbers. If the numbers are generally far from the mean, the standard deviation is large, and vice versa. The formula for standard deviation is the square root of the variance, where variance is the average of the squared differences from the mean. The calculated standard deviation of y in our case is approximately 0.998749169061585.
So, to sum up, for our set of 1.1 million numbers following a standard exponential distribution, the mean is approximately 0.9999921494591629 and the standard deviation is approximately 0.998749169061585.