Download Chapter 2: Random variables

ROHANA
BINTI
ABDUL
HAMID
MADAM
ROHANA
BINTI
ABDUL HAMID
INSTITUT E FOR ENGINEERING MATHEMATICS (IMK)
INSTITUT EMALAYSIA
FOR ENGINEERING
UNIVERSITI
PERLIS MATHEMATICS (IMK)
UNIVERSITI MALAYSIA PERLIS
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2.1Introduction
2.2Discrete probability
distribution
2.3Continuous probability
distribution
2.4Cumulative distribution
function
2.5Expected value, variance and
standard deviation

In an experiment of chance, outcomes occur
randomly. We often summarize the outcome
from a random experiment by a simple number.
Definition 2.1
 A variable is a symbol such as X, Y, Z, x or H,
that assumes values for different elements. If
the variable can assume only one value, it is
called a constant.

A random variable is a variable whose value is
determined by the outcome of a random
experiment.
Example 2.1
A balanced coin is tossed two times. List the elements of
the sample space, the corresponding probabilities and
the corresponding values X, where X is the number of
getting head.
Solution
Elements of
sample space
HH
HT
TH
Probability
X
¼
¼
¼
2
1
1
TT
¼
0
TWO TYPES OF RANDOM
VARIABLES
Discrete
Random
Variables
A random
variable is
discrete if its
set of possible
values consist
of discrete
points on the
number line.
Continuous
Random
Variables
A random
variable is
continuous if
its set of
possible values
consist of an
entire interval
on the number
line.
2.2 DISCRETE PROBABILITY
DISTRIBUTIONS
Definition 2.3:
 If X is a discrete random variable, the function
given by f(x)=P(X=x) for each x within the range
of X is called the probability distribution of X.

Requirements for a discrete probability
distribution:
1) The probability of each value of the discrete random variable
is between 0 and 1, inclusive. That is, 0  P( X  x)  1
2) The sum of all the probabilities is 1. That is,
 P( X  x)  1
xS
Example 2.2
 Check whether the distribution is a probability
distribution.
X
0
1
2
3
4
P(X=x)
0.125
0.375
0.025
0.375
0.125
Solution
4
 P( X  x)  P( X  0)  P( X  1)  P( X  2)  P( X  3)  P( X  4)
0
= 0.125+0.375+0.025+0.375+0.125
= 1.025
1
 so the distribution is not a probability distribution.
Example 2.3
 Check whether the function given by
x2
f ( x) 
for x =1,2,3,4,5
25
can serve as the probability distribution of a discrete
random variable.
Solution
1) The probabilities are between 0 and 1
f(1) = 3/25 , f(2) = 4/25, f(3) =5/25, f(4) = 6/25, f(5)=7/25
Solution
5

1
x2
f ( x)  
25
1
= f (1)  f (2)  f (3)  f (4)  f (5)
1 2 2  2 3  2 4  2 5  2
=




25
25
25
25
25
3
4
5
6
7
=
   
25 25 25 25 25
25
=
25
1
5
# so the given function is a probability distribution of a
discrete random variable.
2.3 CONTINUOUS PROBABILITY
DISTRIBUTIONS
Definition 2.4:
 A function with values f(x), defined over the set
of all numbers, is called a probability density
function of the continuous random variable X if
and only if
b
P(a  X  b)   f ( x) dx
a
for any real constant a and b with a  b
Requirements for a probability density function of
a continuous random variable X:
1) f ( x)  0 for -  x  
2)



f ( x) dx  1. That is the total area under graph is 1.
Example 2.4
Let X be a continuous random variable with the
following probability density function
c(2 x3  5) ,  1  x  1
f ( x)  
, otherwise
0
1) Evaluate c
2) Find P(0  X  1)
Solution
a)
P ( 1  X
1
 1) 
3
c
(2
x
 5) dx

1
1
= c  (2 x 3  5) dx
1
4
= c(
1
2x
 5 x)
4
1
 2(1) 4
  2( 1) 4

= c 
 5(1)   
 5( 1)  
4
 

 4
 11   9  
= c 
    
 2   2  
= c 10 
=1
 c
1
10
b)
1
1
2 x 3  5  dx

10
0
P (0  X  1)  
4
1
1 2x
=
(
 5x )
10 4
0
  2(0) 4

1  2(1) 4
=
 5(1)   
 5(0)  

10  4
  4

1  11 
=
 
10  2 
11
=
20
= 0.55
2.4 CUMULATIVE DISTRIBUTION
FUNCTION

The cumulative distribution function of a discrete
random variable X , denoted as F(X), is
F ( x)  P( X  x)   f (t ) for    x  
tx

For a discrete random variable X, F(x) satisfies the
following properties:
1) 0  F ( x)  1
2) If x  y, then F ( x)  F ( y )

If the range of a random variable X consists of the
values x1  x2  x3  ...  xn , then f ( x1 )  F ( x1 ) and
f ( xi )  F ( xi )  F ( xi 1 ) for i  2, 3,..., n

The cumulative distribution function of a
continuous random variable X is
x
F ( x)  P( X  x) 

f (t ) dt for    x  

Let F  x  be the distribution function for a continuous random
dF ( x)
variable X . Then f ( x) 
 F ( x)
dx
wherever the derivative exists.
Example 2.5 (Discrete random variable)
5 x
Given the probability function f ( x) 
for x  1, 2,3, 4,
10
find F ( x)
Solution
x
1
2
3
4
f(x)
4/10
3/10
2/10
1/10
F(x)
4/10
7/10
9/10
1
Example 2.6 a.(Continuous random variable)
Given the probability density function of a random
variable X as follows;
3 2
 x ,
f ( x)   8
0,
0 x2
otherwise
Find the cumulative distribution function, F(X)
ii) Find P(1  X  2)
i)
Solution
i)
For x  0 ,
x
F ( X )   0dt  0

ii)
For 0  x  2
0
x
3 2
F ( X )   0dt   t dt
8

0
3
t

8
3
x
0
x

8
For x  2 ,
0
2
x
3 2
F ( X )   0dt   t dt   0dt
8

0
2
t3

8
2
0
0,
 3
x
F(X )   ,
8
1,
23

1
8
  x  0
0 x2
x2
iii ) P (1  X  2)
 F (2)  F (1)
3
3
2 1
 
8 8
7

8
Example 2.6 b.(Continuous random variable)
If X has the probability density
k  e 3 x for x  0
f ( x)  
elsewhere
0
Find
i) k
ii) F ( x)
iii) P(0.5  x  1)
Solution

i)
 f  x  dx  1


 k e
0
3 x

e 
dx  k 


3

0
3 x
  1 
 k 0   
  3  
k
 1
3
 k 3
ii) for x  0,
F  x 
x
 0 dt  0

for x  0,
F  x 
0


x
0 dt   3e 3t dt
0
x
 0  3 e 3t dt
0
x
e 
 3


3

0
3t
 1 e 3 x  e 0   1  e 3 x
0
 F  x  
3 x
1

e

for x  0
for x  0
iii) P  0.5  X  1  F 1  F  0.5 

 
 1  e 31  1  e 3 0.5
 1  e 3   1  e 1.5 
 0.173

2.5 EXPECTED VALUE, VARIANCE AND
STANDARD DEVIATION
2.5.1 Expected Value


The mean of a random variable X is also known
as the expected value of X as    X  E ( X )
If X is a discrete
random variable,
If X is a continuous
random variable,
2.5.2 Variance
Var ( X )   2   X2  E (( X   ) 2 ), where
Var ( X )   ( X   ) 2 P ( X  x), in the discrete case,
xS

Var ( X ) 
2
(
X


)
f ( x)dx , in the continuous case when it exists.


Var ( X ) exists if and only if   E ( X ) and E ( X 2 ) both exist, and
then Var ( X )  E ( X 2 )  ( E ( X )) 2
2.5.3 Standard Deviation
The standard deviation is    X  Var ( X )   X2
2.5.4 Properties of Expected Values
• For any constant a and b,
i) E (a)  a
ii) E (bX )  bE ( X )
iii) E (aX  b)  aE ( X )  b
2.5.5 Properties of Variances
For any constant a and b,
i) Var (a )  0
ii) Var (bX )  b Var ( X )
2
iii) Var (aX  b)  a 2Var ( X )
Example 2.7
Find the mean, variance and standard deviation
of the probability function
x
f ( x) 
for x  1, 2,3, 4
10
Solution
Mean:
n
E  X    x  f  x
i 1
4
x
 x
10
i 1
1
2
3
4
 1  2   3   4 
10
10
10
10
30

3
10
Varians:
E  X 2    x2  f  x 
n
i 1
4
x
x 
10
i 1
2
3
2 1
2
2
2 4
1   2  3   4 
10
10
10
10
 10
2
Var ( X )  E ( X 2 )  ( E ( X )) 2
 10  32  1
 X  Var ( X )  1
Example 2.8
Let X be a continuous random variable with the
Following probability density function
3
 x(2  x), 0  x  2
f ( x)   4
0
, otherwise
Find
a) E ( X )
b) Var ( X )
Solution
a) E  X

 
x f
 x
dx

2


x
0
3
x  2  x  dx
4
2
3

4

3

4
2
x2
2  x
dx
0
2
3
2
x

x
dx

0
3  2 x3
x4 




4 3
4 
2
0
3

3  2  2 
24 

  0



4
3
4 




34

  1
43
b)E  X

2
 
x2  f
 x
dx

2


x2 
0
3
x  2  x  dx
4
2
3

4

3

4
2
x3
2 
x  dx
0
 2x
3
 x 4 dx
0
3  2x4
x5 




4 4
5 
2
0
4

3  2  2 
25 

  0



4 
4
5 




38
6




45
5
Var ( X )  E ( X 2 )  ( E ( X )) 2
6
1

 12 
5
5