Lecture11_Chapter3

# Lecture11_Chapter3 - Lecture 11 Chapter 3 Wednesday,...

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Lecture 11 – Chapter 3 Wednesday, October 1 st

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Probability distribution ± The probability distribution or probability mass function (pmf) of a discrete random variable is defined as a rule that assigns probability to all possible outcomes of the random variable by ( ) ( ) px PX x = =
Example ± Find the probability distribution for the outcome of a toss of a coin

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Example ± Find the probability distribution for the outcome of a roll of a die
Example ± Let X be the number of heads when we toss a coin 3 times. Find the probability distribution of X.

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Draw pictures of the pmf’s found in the previous 3 examples
Parameter ± There are cases that the probability distribution depends on a quantity that can be assigned any possible value. Such a quantity is called a parameter , of the distribution. A different value for the parameter gives a different probability distribution and all those probability distributions constitute a

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## This note was uploaded on 09/23/2009 for the course STAT 318 taught by Professor Staff during the Fall '08 term at Penn State.

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Lecture11_Chapter3 - Lecture 11 Chapter 3 Wednesday,...

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