Exam 2 - Review Chapters 14 - 18

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)

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)

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

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

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