das said:Help?
Suppose the random variable Y has an EXP(2) distribution. What is P(Y > 1)? (Round to four decimal places as appropriate.)
das said:I don't think there are any restrictions on what functions I can or can't use
The Exponential Distribution is a probability distribution that describes the time between events in a Poisson process. It is used to model situations where events occur continuously and independently at a constant average rate.
The Exponential Distribution has two key parameters: the rate parameter (λ) and the mean (μ). The rate parameter determines the average rate of events occurring, while the mean represents the expected time between events. It is a continuous distribution with a skewed right shape and ranges from 0 to infinity.
The Exponential Distribution is unique because it models the time between events, rather than the number of events in a given time period. It also has a constant failure rate, meaning the probability of an event occurring in a specific time interval does not change over time.
The Exponential Distribution is commonly used in fields such as engineering, finance, and healthcare. It can be used to model the time between equipment failures, the time between customer arrivals, and the time between medical treatments. It is also used in reliability analysis and to study the lifespan of products.
The probability density function (PDF) for the Exponential Distribution is f(x) = λe^-λx, where λ is the rate parameter and x is the time between events. The cumulative distribution function (CDF) is F(x) = 1 - e^-λx. These equations can be used to calculate probabilities and make predictions about future events.