Answer:
The probability of SFS and SSF are same, i.e. P (SFS) = P (SSF) = 0.1311.
Step-by-step explanation:
The probability of a component passing the test is, P (S) = 0.79.
The probability that a component fails the test is, P (F) = 1 - 0.79 = 0.21.
Three components are sampled.
Compute the probability of the test result as SFS as follows:
P (SFS) = P (S) × P (F) × P (S)
Compute the probability of the test result as SSF as follows:
P (SSF) = P (S) × P (S) × P (F)
Thus, the probability of SFS and SSF are same, i.e. P (SFS) = P (SSF) = 0.1311.
Looks like a badly encoded/decoded symbol. It's supposed to be a minus sign, so you're asked to find the expectation of 2<em>X </em>² - <em>Y</em>.
If you don't know how <em>X</em> or <em>Y</em> are distributed, but you know E[<em>X</em> ²] and E[<em>Y</em>], then it's as simple as distributing the expectation over the sum:
E[2<em>X </em>² - <em>Y</em>] = 2 E[<em>X </em>²] - E[<em>Y</em>]
Or, if you're given the expectation and variance of <em>X</em>, you have
Var[<em>X</em>] = E[<em>X</em> ²] - E[<em>X</em>]²
→ E[2<em>X </em>² - <em>Y</em>] = 2 (Var[<em>X</em>] + E[<em>X</em>]²) - E[<em>Y</em>]
Otherwise, you may be given the density function, or joint density, in which case you can determine the expectations by computing an integral or sum.
Answer:
5
Step-by-step explanation:
Because if you divide them all you see that in 5 you don't get a whole number
the answer os 5xx entendida
