1. Exam 2 - Review
Chapters

2. Chapter 14: Randomness & Probability
P(A) =
0 ≤ P(A) ≤ 1
P(A) = 1 – P(Ac)
A,B disjoint: P(A or B) = P(A) + P(B)
A,B independent: P(A and B) = P(A) x P(B)

3. Chapter 15: Probability Rules
P(A or B) = P(A) + P(B) – P(A and B)
P(A and B) = P(A) x P(B | A)
Independence occurs when P(B | A) = P(B)

4. Chapter 16: Random Variables
Probability Model using table
µ = E(X) =
σ2 = Var(X) =
σ = SD(X) =
Impact of shift/stretch on mean and variance

5. Chapter 17: Binomial Model
Binom(n,p): P(X = x) = nCx px qn-x
Expected Value: µ = np
Standard Deviation: σ =
Success/Failure condition: Binomial model can be approximated by Normal if we expect at least 10 successes and 10 failures
10% Condition: sample size must be no more than 10% of population to assume independence

6. Chapter 18: Sampling Distribution Models
Central Limit Theorem
Sampling Distribution can be described using Normal model
Conditions:
Randomization
10% Condition
Success/Failure Condition
Large enough sample
Proportions:
Means: Mean: µ