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(Getting rid of this idiotic phrase, which is not the correct title of the linked page.) 
(→Definition for two random variables: cleaning up notation) 

Example of joint cumulative distribution function:
For two continuous variables ''X'' and ''Y'':
For two discrete random variables, it is beneficial to generate a table of probabilities and address the cumulative probability for each potential range of ''X'' and ''Y'', and here is the example<ref>{{Cite weburl=https://math.info/Probability/Joint_CDF/title=Joint Cumulative Distribution Function (CDF)website=math.infoaccessdate=20191211}}</ref>:
given the joint probability density function in tabular form, determine the joint cumulative distribution function.
{ class="wikitable"

''Y'' = 2
''Y'' = 4
''Y'' = 6
''Y'' = 8

''X'' = 1
0
0.1
0.1

''X'' = 3
0
0
0

''X'' = 5
0.3
0
0.15

''X'' = 7
0
0
0
}
Solution: using the given table of probabilities for each potential range of ''X'' and ''Y'', the joint cumulative distribution function may be constructed in tabular form:
{ class="wikitable"

''Y'' < 2
2 ≤ ''Y'' < 4
4 ≤ ''Y'' < 6
6 ≤ ''Y'' < 8
''Y'' ≤ 8

''X'' < 1
0
0
0

1 ≤ ''X'' < 3
0
0
0.2

3 ≤ ''X'' < 5
0
0
0.4

5 ≤ ''X'' < 7
0
0.3
0.85

''X'' ≤ 7
0
0.3
}
<br />
===Definition for more than two random variables===
For <math>N</math> random variables <math>X_1,\ldots,X_N</math>, the joint CDF <math>F_{X_1,\ldots,X_N}</math> is given by
