Chapter 17 Probability Models By: Alex Woerner, and Nils Kosmo Bernoulli Trials ● The basis for all probability models in this chapter ● Bernoulli trials have: 1. Two possible outcomes (success and failure) 2. p, the probability of success as a constant 3. Independent trials Geometric Probability Model ● Tells us the probability for a random variable that counts the number of Bernoulli trials until the first success. ● Completely specified by one parameter, p ● Denoted Geom(p) ● p = probability of success ● q = 1 - p = probability of failure ● X = # of trials until the first success occurs P(X = x) = qx-1p Independence ● Bernoulli trials must be independent. ● Bernoulli trials are not independent when we don’t have an infinite population. ● 10% Condition: Bernoulli trials must be independent. If this assumption is violated, it is ok to proceed as long as the sample is smaller than 10% of the population. Binomial Model ● Tells the probability for a random variable that counts the number of successes in a fixed number of trials. ● Two parameters: n, number of trials, and, p, probability of success. Denoted Binom(n,p) n Ck = n!/k!(n-k)! Binomial Model Cont. ● ● ● ● n = number of trials p = probability of success q = 1-p = probability of failure X = # of successes in n trials P(X = x) = nCx px qn-x Normal Model ● Used when dealing with large numbers of trials ● Success/Failure Condition must hold to use Normal Model ● Success/Failure Condition: Expect at least 10 successes and 10 failures np>10 and nq>10 Continuous Random Variable ● When using Normal Model to approx. Binomial Model, we are using a continuous random variable to approx. a discrete random variable. ● When using Normal Model, we no longer calculate the probability that the random variable equals a particular value, but only that it lies between two values. Homework Problem #13 Homework Problem #15 Work/Answers for #13 & #15