Suppose that you are a student-employee in the Accounting
Department of the Business
School and they agree to pay you using the Random Pay system. Each
week the Chair
flips a coin. If it comes up heads, your pay for the week is $80;
if it comes up tails your
pay for the week is $40. Your friend is working for the Engineering
Department and
makes $65 per week. If your total earning in 100 weeks is a random
variable that
follows a Normal probability distribution, the probability that
your total earnings in 100
weeks are more than hers is approximately:
Total earning of friend in 100 weeks is given as: 65*100 = 6500
For us, the probability distribution of earnings in a week is
given as:
P(X = 80) = P(X = 40) = 0.5
E(X) = 0.5*(80 + 40) = 60
E(X2) = 0.5*(802 + 402) =
4000
Therefore, Var(X) = E(X2) - [E(X)]2 = 4000 -
602 = 400
Therefore the distribution for 100 weeks mean earning is given here as: (Using Central limit theorem)
The required probability now is computed here as:
Converting it to a standard normal variable, we get here:
Getting it from the standard normal tables, we get here:
Therefore 0.0062 is the required probability here.
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