2. Summary
선형변형 확률모형의 경우 E(aX) 는 aE(X)와
같이 선형 변환되고, Var(aX) 는 a^2Var(X)와
같이 제곱으로 선형변경됨.
확률독립사건은 + - 의 경우는 총합(Sigma) 연
산이므로 E(X + Y) = E(X) + E(Y) 로 분리할 수
있다.
3. Formulas
Statistic Shortcut or formula
E(aX + b) aE(X) + b
Var(aX + b) a^2 Var(X)
E(X) Sigma x P(X=x)
E(f(X)) Sigma f(x) P(X=x)
Var(aX - bY) a^2 Var(X) + b^2 Var(Y)
Var(X) E(X-mu)^2 = E(X^2) - mu^2
E(aX - bY) aE(X) - bE(Y)
E(X1 + X2 + X3) 3 E(X)
Var(X1 + X2 + X3) 3 Var(X)
E(X^2) Sigma x^2 P(X=x)
Var(aX - b) a^2 Var(X)
7. Independent
Observation
E(X1 + X2 + ... Xn) = nE(X)
Var(X1 + X2 + ... Xn) = nVar(X)
X1 + X2 is not the same as 2X.
compare Var(X1 + X2) vs Var(2X)
8. Transform or
Observation?(Q)
Linear Independent
transform obseration
The amount of coffee in an extra large cup of coffee;
o
X is the amount of coffee in a normal-size cup
Drinking an extra cup of coffee per day;
o
X is the amount of coffee in a cup
Finding the net gain from buying 10 lottery tickets;
o
X is the net gain of buying 1 lottery ticket
Finding the net gain from a lottery ticket after the price of tickets goes up;
o
X is the net gain of buying 1 lottery ticket.
Buying an extra hen to lay eggs for breakfast;
o
X is the number of eggs laid per week by a certain breed of hen