MATH 302 Lecture Notes - Lecture 4: Cumulative Distribution Function, Random Variable

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30 Jul 2016
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Cumulative distribution function
All the different variables have a cumulative distribution function, which simply
adds up the probabilities of all the values of the variable below the particular
value of X. So if X = 3, then the cdf will be the probability of 1 + probability of 2 +
probability of 3.
Just like in a cumulative graph, it just provides the values of the cumulative
probabilities so far.
So for example,
The pdf of a function is equal for all values in the range and 0 otherwise. There
are 5 values in the range from 1 to 5 What is the cdf?
The cdf would be
X = 0 0
X = 1 0.20
X = 2 0.40
X = 3 0.60
X = 4 0.80
X = 5 1
X = 6,7,8 1
The cdf is represented by F(x)
The shape of the graph for a cdf of a discrete random variable
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