Chapter 17

Chapter 17 - Chapter 17 Thinking about Chance In this...

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Chapter 17 Thinking about Chance In this chapter Introduction to probability Probability rules Independence Probability from a contingency table Introduction to probability We will look at situations with more than one outcome for which we cannot know the outcome with certainty. Therefore, we will deal with probability. Sample space – the set of all possible outcomes from an experiment Example Identify the sample space for each of the following Roll a die } 6 , 5 , 4 , 3 , 2 , 1 { space sample = Flip a coin } T H, { space sample = The above examples illustrate a common way to denote a sample space. Also notice that when you roll a die you get a discrete quantitative variable and when you flip a coin you get a qualitative variable. These are the types of data we will deal with in the next few chapters. We already talked about probability for a continuous quantitative variable in chapter 13 when looking at the area under a density curve. Event – a subset of the sample space Example Suppose you roll a die and event A is defined by getting an even number. We would represent this event as follows. } 6 , 4 , 2 { A = Probability (of an event A) – denoted ) ( A P , is the expected proportion of occurrences of A if the experiment were performed a large number of times (keep in mind A is just an example, we could call the event whatever we wanted) outcomes of # Total event in the outcomes of # event an of y Probabilit =
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We will look at a couple counting principal problems in order to understand how to identify the number of outcomes when there are a series of operations in an experiment. Example 1
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This note was uploaded on 07/12/2011 for the course STATS 200 taught by Professor Bradley,w during the Fall '10 term at KCTCS.

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Chapter 17 - Chapter 17 Thinking about Chance In this...

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