Though its variance is one over its parameter (*) squared, its expected value is one over that parameter, which is often represented with a lambda.
Its skewness is 2, which can be inferred from the fact its moment generating function is "p divided by the quantity p minus t", where p is its single parameter and t is time. Given a series of random variables characterized by this distribution, the sum of those random variables fits the Erlang Distribution. Though its variance is one over its parameter (*) squared, its expected value is one over that parameter, which is often represented with a lambda. This distribution is the continuous analogue of the geometric distribution, which means that it too, is memoryless. For 10 points, name this probability distribution that is closely modeled by the function "e to the x."
Its skewness is 2, which can be inferred from the fact its moment generating function is "p divided by the quantity p minus t", where p is its single parameter and t is time.
exponential distribution