Suppose that {Xk} k=1,...,n are i.i.d ∼ Exp(λ), the pdf is given by f(x) = λe^(-λx), x > 0. Let Y1 be the 1st–order statistics. Given a > 0, evaluate P(Y1>a/n)
Suppose that {Xk} k=1,...,n are i.i.d ∼ Exp(λ), the pdf is given by f(x) = λe^(-λx), x > 0. Let Y1 be the 1st–order statistics. Given a > 0, evaluate P(Y1>a/n)
Suppose that {Xk} k=1,...,n are i.i.d ∼ Exp(λ), the pdf is given by f(x) = λe^(-λx), x > 0. Let Y1 be the 1st–order statistics. Given a > 0, evaluate P(Y1>a/n)
Suppose that {Xk} k=1,...,n are i.i.d ∼ Exp(λ), the pdf is given by
f(x) = λe^(-λx), x > 0.
Let Y1 be the 1st–order statistics.
Given a > 0, evaluate P(Y1>a/n)
Definition Definition Probability of occurrence of a continuous random variable within a specified range. When the value of a random variable, Y, is evaluated at a point Y=y, then the probability distribution function gives the probability that Y will take a value less than or equal to y. The probability distribution function formula for random Variable Y following the normal distribution is: F(y) = P (Y ≤ y) The value of probability distribution function for random variable lies between 0 and 1.
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