MATH 1780 Lecture Notes Chapter 2 Section 3

# MATH 1780 Lecture Notes Chapter 2 Section 3 - The notion of...

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The notion of probability requires three elements: A target population (either conceptual or real) from  which observable outcomes are obtained Meaningful categorization of these outcomes A random mechanism for generating outcomes

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Example 1: Imagine tossing a single fair die on a  flat surface and the number on the top face is  recorded.  This is an example of a probabilistic  situation, because the outcome of the toss can not  be determined before the toss.   A situation like the one in the previous example is  called a  random experiment .  We will be  analyzing this random experiment frequently  throughout the course.
sample space  S is a set that includes all  possible outcomes for a random experiment, listed  in a mutually exclusive and exhaustive manner. Mutually exclusive means that the elements of the  sample space do not overlap, and exhaustive  means that all possible outcomes are accounted  for. From the die toss in the previous example, two  possible samples spaces would be: S 1  = {1, 2, 3, 4, 5, 6} S 2  = {even, odd}

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An  event  is any subset of a sample space. Going back to the die tossing experiment, give a  set notation definition of each of the following  events, letting S = {1, 2, 3, 4, 5, 6}: A is “an even number”
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MATH 1780 Lecture Notes Chapter 2 Section 3 - The notion of...

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