Answer:
a) [tex] E(Y) = \sum_{i=1}^n Y_i P(Y_i)[/tex]
And replacing we got:
[tex] E(Y) =0*0.60 +1*0.25 +2*0.1 +3*0.05 = 0.60[/tex]
b) [tex] E(Y^2) =0^2*0.60 +1^2*0.25 +2^2*0.1 +3^2*0.05 = 1.1[/tex]
And then the expected value would be:
[tex] E(100Y^2) = 100*1.1= 110[/tex]
Step-by-step explanation:
We assume the following distribution given:
Y 0 1 2 3
P(Y) 0.60 0.25 0.10 0.05
Part a
We can find the expected value with this formula:
[tex] E(Y) = \sum_{i=1}^n Y_i P(Y_i)[/tex]
And replacing we got:
[tex] E(Y) =0*0.60 +1*0.25 +2*0.1 +3*0.05 = 0.60[/tex]
Part b
If we want to find the expected value of [tex] 100 Y^2[/tex] we need to find the expected value of Y^2 and we have:
[tex] E(Y^2) = \sum_{i=1}^n Y^2_i P(Y_i)[/tex]
And replacing we got:
[tex] E(Y^2) =0^2*0.60 +1^2*0.25 +2^2*0.1 +3^2*0.05 = 1.1[/tex]
And then the expected value would be:
[tex] E(100Y^2) = 100*1.1= 110[/tex]