Reading from text, Lecture 5

Chapter 5

This chapter introduces six discrete probability distributions; we will cover the first three on 9/12 and the remaining three on 9/14.

5.2

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)

5.3

Introduction

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.

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