Reading from text, Lecture 5
This chapter introduces six discrete probability distributions; we will cover
the first three on 9/12 and the remaining three on 9/14.
- The Bernoulli Process
- Binomial Distribution
- Example 5.1
- Note that the table of Cumulative Binomial probabilities is Table A.1
- Example 5.2
- Example 5.3 - Note that part b is an example of "chaining" probability
calculations, where both parts are binomial distributions, the second part
based on the result of the first (part a).
- Theorem 5.1 - Note that you have the tools now to do this derivation yourself,
should you be so inclined
- Example 5.4 - Notice that most of the real-world examples you will encounter
are, strictly speaking, violating the "independence" assumption for the Bernoulli trials.
We can discuss this in class.
- Example 5.6 - This example looks ahead to hypothesis testing, and is similar
to our first-day Common Sense Statistics Quiz, Q_01b, no. (4) question. The difference
is that rather than "imagining" whether or not the conjecture is correct, the
example puts a probability on the result given the conjecture. Then, we
can compare that probability to our idea about what is likely.
- Multinomial Experiments ... (all)
- Example 5.8
- Definition of the Hypergeometric Distribution
- Example 5.9
- Theorem 5.2
- Example 5.10
- Example 5.11
- Relationship to the Binomial, and Example 5.12 - This is the Binomial
Approximation to the Hypergeometric. We will be seeing other examples of
approximations later in this chapter and later in the course.