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Department
Statistical Sciences
Course Code
STA257H1
Professor
N/ A

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RANDOM VARIABLES AND EVENTS
Definition: Random Variable
A random variable is a variable that is random. They are denoted X, Y, Z.
Note
A random variable which is constant will be called a constant random variable.
Example
Roll a fair die and let X denote the number of dots.
X is a random variable. Its possible values are 1, 2, 3, 4, 5, 6.
Observing X leads to data or observed value. We will denote a typical observation by x.
Definition: Event
In general, an event is a statement involving random variables. They are denoted A, B, C, D.
Notation
{X is even} means the event that X is even.
Note
Events either occur or they dont.
Notes
1) Events may be thought of as collections/sets of outcomes.
2) The sure event (denoted S or ) is the set of all possible outcomes.
3) S includes all the outcomes so that any event A is made up of outcomes drawn from S. A is a subset of S and
we write SA
.
4) The impossible event or empty set (denoted
φ
) is the event which consists of no possible outcomes and hence
never occurs.
Notice: SA
φ
.
5) We can talk about any function of X, say,
(
)
Xg. Examples: 2
X
, Xsin,
(
)
XeXexp=, etc.
Definition: The Indicator Random Variable or Bernoulli Random Variable
If A is an event, then we define the indicator random variable or Bernoulli random variable for/of A to be
=otherwise 0
occurs if 1A
IA.
Example
Let X be a random variable. Measure it an infinite number of times to get ,,, 321 xxx .
The average will be the expected value or expectation of X, denoted
(
)
XE .
If X denotes marks, then
{
}
{
}
0pass == XA .
The indicator random variable might look like: ,0,1,0,0.
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Definition
The proportion or the relative frequency of 1s will be the probability of A, denoted
(
)
AP .
Notice
(
)
(
)
A
IEAP =.
MORE ON EVENTS AND RANDOM VARIABLES
Events consist of outcomes.
Every event is a subset of S.
Sometimes the same event has different descriptions, so we might want to show something like:
BA
AB
BA =
.
How to show
B
A
?
Take an arbitrary element of A.
Show that it is an element of B, i.e. let Ax
and show Bx
.
From our point of view,
B
A
means BA
.
Two events are equal (i.e. the same) if BA
and AB
.
Random Variables
How to show two random variables X and Y are equal? They are equal if they are equal all the time.
Example
Let A1 and A2 be disjoint events (i.e. no outcomes in common, no overlap, etc.). Look at the event 21 AA
(the event that at least one of the As occurs).
The indicator random variable of this new event is 21 AA
I or
(
)
21 AAI. Clearly,
2121 AAAA III +=
.
Notes
1)
=
=
21
1
AAA
i
i is the event that at least one of the As occurs.
2) If the As are disjoint, then a common notation for
++=
=
21
1
AAA
i
i.
3)
2121
1
AAAAA
i
i==
=
is the event that all of the As occur.
COMPLEMENTS
Definition
The complement of an event A is the event consisting of all outcomes not in A. It is denoted c
or
.
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Example
Toss a coin n times and set heads ofnumber =Y.
{
}
{
}
1head oneleast at == YA.
{
}
{
}
{
}
1 tailsallhead no ==== YAc.
Set
{
}
first toss on the head
1=A,
{
}
tosssecond on the head
2=A, etc.
{
}
mYAAA m==
21 .
{
}
1
21 =YAAA m
.
(
)
{
}
c
m
ccc
mAAAYAAA 2121 0===.
In General
i
c
i
c
i
iAA =
and
i
c
i
c
i
iAA =
Morgans Laws.
(
)
AA c
c=.
In terms of indicator random variables,
1=+ A
AII c.
m
i
i
AA
A
III
1
1
=
=
.
212121 AAAAAA IIII += .
BERNOULLI (P) RANDOM VARIABLES
Definition
X is a Bernoulli (p) random variable if X can only take on either 0 or 1 and
(
)
pXP ==1.
Note
1) Let X be Bernoulli (p) (X ~ Bernoulli (p)). Now measure it an infinite number of times. You end up with a list
of 0s and 1s. The proportion of 1s will be p and the proportion of 0s will be q.
2) Now,
(
)
pXE = (1st moment of X),
(
)
pXE =
2 (2nd moment of X),
(
)
pXE n=.
(
)
(
)
(
)
122 qpEX+= ,
(
)
(
)
(
)
133 qpEX+= .
3) If s is a dummy variable, then
(
)
qpssEX+= . Call
(
)
X
sE the probability generation function denoted by
(
)
sG. This function is in fact defined for any count random variable.
KOLMOGOROV AXIOMS/LAWS OF PROBABILTY
1)
(
)
0AP ;
(
)
1=SP.
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