Lecture 7 (part I)

# Lecture 7 (part I) - Lecture 7 Material Covered in This...

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Lecture 7 Material Covered in This Lecture: * Chapter 4, Section 4.1-4.2: Randomness, Probability Models 1. Basic Concepts (1) Random phenomenon: A random phenomenon has outcomes that we cannot predict but that have a regular distribution in very many repetitions. (Example1). Tossing a fair coin; You cannot predict what will be the result: But there is nonetheless a regular pattern that emerges clearly only after many repetitions. This graph is the proportion of head versus number of tosses for 5000 tosses . The proportion of tosses that give head varies as we make more tosses. Eventually, however, the proportion approaches 0.5.

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Other examples of random phenomenon (E2). Rolling a fair die; (E3). Drawing a card from a shuffled deck; (E4). Sampling a number of customers for an opinion survey or (E5). Quality inspection of items from a production line. (2). Sample Space: Example: E2: Rolling a fair die, observe which number appears on the die. S={1,2,3,4,5,6}
(3). Event: Notation: A, B, C, etc. Example:

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## This note was uploaded on 07/25/2008 for the course STT 421 taught by Professor Nane during the Summer '08 term at Michigan State University.

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Lecture 7 (part I) - Lecture 7 Material Covered in This...

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