# 09_06 - STAT 400 Fall 2011 Examples for A random variable...

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STAT 400 Examples for 09/06/2011 Fall 2011 A random variable associates a numerical value with each outcome of a random experiment. A random variable is said to be discrete if it has either a finite number of values or infinitely many values that can be arranged in a sequence. If a random variable represents some measurement on a continuous scale and therefore capable of assuming all values in an interval, it is called a continuous random variable. The probability distribution of a discrete random variable is a list of all its distinct numerical values along with their associated probabilities: x f ( x ) x 1 x 2 x 3 x n f ( x 1 ) f ( x 2 ) f ( x 3 ) f ( x n ) ! ! 1) for each x , 0 f ( x ) 1. 2) x f x all ) ( = 1. 1.00 Often a formula can be used in place of a detailed list. 1. A balanced (fair) coin is tossed twice. Let x f ( x ) X denote the number of H's. Construct the probability distribution of X.

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2. Suppose a random variable X has the following probability distribution: x f ( x ) 10 0.20 11 0.40 12 0.30 13 0.10 a)
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## This note was uploaded on 11/07/2011 for the course STAT 400 taught by Professor Kim during the Fall '08 term at University of Illinois, Urbana Champaign.

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09_06 - STAT 400 Fall 2011 Examples for A random variable...

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