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MGMT305-Lec2 - Chapter5 s s s RandomVariables s...

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  1              Slide Chapter 5  Binomial Probability Distribution Random Variables Discrete Probability Distribution Expected Value and Variance Properties of Expectation and Variance Finance Example Binomial Probability Distribution . 1 0 . 2 0 . 3 0 . 4 0  0         1         2         3         4
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  2              Slide Random Variables A random variable  is a numerical description of the  outcome of an experiment. A random variable can be classified as being either  discrete or continuous depending on the numerical values  it assumes. A discrete random variable  may assume either a finite  number of values or an infinite sequence of values. A continuous random variable  may assume any numerical  value in an interval or collection of intervals.
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  3              Slide Examples Example 1:   Suppose there are 10 traffic lights when you   drive  from your apartment to school. Let X = number of red lights you encounter on a day. What values does X take?  Is X a discrete or continuous random variable? Example 2:   Suppose maximum waiting time at a traffic light is  ½  minutes.  X = Total time you wait at all 10 traffic lights. What values does X take? 
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  4              Slide  The probability distribution  for a random variable  describes how probabilities are distributed over  the values of the random variable.  We can describe a discrete probability distribution  with a table, graph, or equation. Discrete Probability Distributions
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  5              Slide  The probability distribution is defined by a  probability function , denoted by  f ( x ), which provides  the probability for each value of the random variable.  The required conditions for a discrete probability  function are: Discrete Probability Distributions f ( x ) >  0 Σ f ( x ) = 1
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  6              Slide    a tabular representation of the probability      distribution for TV sales was developed.
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