ECON 2B03 Lecture Notes - Lecture 11: Random Variable, Discrete Event Simulation
19 Jan 2014
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Document Summary
Unlike their discrete counterparts, continuous random variables are uncountable" and can assume any value on the real number line. Rather than looking at p(x =x) (i. e. the probability of any specific occurrence), we instead look at p( a < x < b), i. e. , the probability that a continuous random variable lies in an interval. Commonly shows probabilities with ranges of values along the continuum of possible values that the random variables might take on. Let some smooth curve represent how the probability of a continuous random variable x is distributed. If the smooth curve can be represented by a formula f(x), then the function f(x) is called the probability density function. Areas will be seen to play an important role in determining probabilities. The total area under the probability density function (just as under the relative frequency histogram) must therefore equal 1.
