1/28/2009

Chapter 5

Case Study 5-3 All Star Baseball

• Spinners as a randomization device (discuss spinner for Babe Ruth on p. 136)
• How would you represent Mike Schmidt's spinner using Minitab?

Case Study 5-4 Strat-O-Matic Baseball

• Theorem of total probabilities
• Conditional probability

Chapter 6

Case Study 6-1 The Binomial Distribution and Hits Per Game

• Binomial probabilities
• Independence
• Expected counts
• Calculations for Table 6.2 on p. 165 with Minitab

Binomial Experiments

Each random trial can result in one of only two possible outcomes.  This is called a Bernoulli trial.

We collect data from Bernoulli trials satisfying the following:

there are n trials;

the n trials are independent; and

the probability of “success” remains constant from trial to trial.

Binomial Distribution - If X is the number of successes in n independent Bernoulli trials, then P(X=k)=n!/[k!*(n-k)!]*p^k*(1-p)^n-k, for k=0, 1, ..., n.

Binomial Distributions - Calc > Probability Distributions Binomial

• Probability - P(X=x)
• Cumulative Probability - P(X<=x)
• Inverse Cumulative Probability - Find percentiles

Class Exercises

Leadoff Exercise, 5.1, 5.5, 5.7, 5.11, 5.13

Reading Assignment - Please complete your reading of Chapter 6 for class on Friday.