Answer:
The probability that a person is guilty given that he or she denies the knowledge of the error is 0.6068.
Step-by-step explanation:
The Bayes' theorem states that the conditional probability of an event E[tex]_{i}[/tex], belonging to the sample space S, given that another event A has already occurred is:
[tex]P(E_{i}|A)=\frac{P(A|E_{i})P(E_{i})}{P(A|E_{1})P(E_{1})+P(A|E_{2}P(E_{2})+...+P(A|E_{n}P(E_{n})}[/tex]
Denote the events as follows:
X = illegal deduction is filed
Y = knowledge of the error is denied.
The information given is:
P (Cheating) = 0.06
P (Actual error) = 0.03
P (Y|X) = 0.76
Compute the probability of X as follows:
[tex]P(X)=\frac{P(Cheating)}{P(Cheating)+P(Actual\ error)}=\frac{0.06}{0.06+0.03}=0.67[/tex]
The probability that a person who is not guilty will deny the knowledge of the error, is:
[tex]P (Y|X^{c})=1[/tex]
Compute the value of P (X|Y) as follows:
[tex]P(X|Y)=\frac{P(Y|X)P(X)}{P(Y|X)P(X)+P(Y|X^{c})P(X^{c})}[/tex]
[tex]=\frac{0.76\times 0.67}{(0.76\times 0.67)+(1\times (1-0.67))}[/tex]
[tex]=\frac{0.5092}{0.5092+0.33}[/tex]
[tex]=0.6068[/tex]
Thus, the probability that a person is guilty given that he or she denies the knowledge of the error is 0.6068.