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Probability theory and statistics
Chapter 2
Random variables
Random variable
Random variable and probabilities
Problem: Events defined by a random variable
Distribution functions
CDF, cumulative distribution function
Discrete random variable and probability mass function
Discrete random variable: probability mass function versus cumulative distribution function
Continuous random variable and the probability density function
The pdf and the cdf of a continuous random variable
The standard normal random variable
Standard normal
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CDF, cumulative distribution function
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Summary
Given: A (real-valued) random variable
X
X
.
The
cumulative distribution function
F
:
R
→
[
0
,
1
]
F
:
R
→
[
0
,
1
]
is defined as
F
(
x
)
=
P
(
X
≤
x
)
F
(
x
)
=
P
(
X
≤
x
)
Properties of the CDF function:
F
F
is an increasing function
0
≤
F
(
x
)
≤
1
0
≤
F
(
x
)
≤
1
lim
x
→
∞
F
(
x
)
=
1
lim
x
→
∞
F
(
x
)
=
1
lim
x
→
−
∞
F
(
x
)
=
0
lim
x
→
−
∞
F
(
x
)
=
0