Questions Flashcards
Probability
0<=P(event)<=1
Objective Approach
no. occurences/ total trials or outcomes
Adding Mutually Exclusive Events
P(A) + P(B)
Adding Not Mutually Exclusive Events
P(A) + P(B) - P(A and B)
Statistically Independent : Joint
P(A) x P(B)
Statistically dependent : Joint
P(A\B) x P(B)
Statistically Independent : Conditional
P(A\B) = P(A)
Statistically dependent : Conditional
P(A\B) = P(AB) / P(B)
Normal distrib approx. probabilities
68(.268) /95(.45)/ 99.7(3)
Bayes Theorem
(P(B\A)P(A) / (P(B\A)P(A) + P(B/A’)P(A’)
Mean/ Expected Value
E xiP(xi)
Standard Variance
(E(ci-mu)^2)/n-1
Variance
E [Xi-E(x)^2} P(x)
Binomial Distribution (BD): Successes
(n!)/(r!(n-r)!) *P^r q^(n-r)
BD: expected value /mean
np
BD: Variance
npq
Normal distrib f(x)
check paper
z score
(x-mu)/standard deviation
Coercion of realism
WA=COR(Max. Row) + (1-CoR)(Min.Row) ===Highest Value
Equally Likely
(Max+Min)/no.
Minimax Regret
Best Payoff-Each payoff ===Minimum Value
Expected Monetary Value
(payoff 1t state)(Psecond)+(Payoff 2nd)(Psecon)+…n
Expected Value of Perfect Information
EVwPI-Max. Emv
EVwPI
(Max each times probs)
EOL
=Max Emv
EVPI=Min EOL
Minimax Regret x EmV
Sensitivity analysis
fav(p)-Unfav(1-p) = Fav(p) -Unfav+unfave(p)
Expected Value of sample info
(Ev with sample infor+cost)- Ev wout SI
eV w/ Si assum no cost)(Ev best decision wout SI
Determinants 2x2
detA= A11(A22) -A21(A12)
Determinants 3x3
A11\C11-A12\C12+A13\C13\
Cofactor
Cij = ((-1)^(i+j))(Mij or det A) check papaer
Inverse
check paper
Inverse matrix
1/det* inversed matrix
Exponential Smoothing
Fv(t+1) = Fvt + constant(Yvt-Fvt)
Trend Projection (3 formulas)
Y=b1+b2X
B1 =xy -((Ex)(Ey)/n) / E(x^2)-(((Ex)^2)/n))
B2 = yu- B1xu
Mean absolute deviation
Total Error/n