1. EQT 272 PROBABILITY AND STATISTICS
ROHANA BINTI ABDUL HAMID
INSTITUT E FOR ENGINEERING MATHEMATICS (IMK)
UNIVERSITI MALAYSIA PERLIS
ROHANA BINTI ABDUL HAMID
INSTITUT E FOR ENGINEERING MATHEMATICS (IMK)
UNIVERSITI MALAYSIA PERLIS
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2. CHAPTER 2
RANDOM VARIABLES

3. 2. RANDOM VARIABLES 2.1Introduction
2.2Discrete probability distribution
2.3Continuous probability distribution
2.4Cumulative distribution function
2.5Expected value, variance and standard deviation

4. INTRODUCTION
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.

5. Elements of sample space
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
Probability X HH 2 HT 1 TH TT

6. Discrete Random Variables Continuous Random Variables
TWO TYPES OF RANDOM VARIABLES
A random variable is discrete if its set of possible values consist of discrete points on the number line.
Discrete Random Variables
A random variable is continuous if its set of possible values consist of an entire interval on the number line.
Continuous Random Variables

7. EXAMPLES Examples of continuous random variables:
Examples of discrete random variables:
-number of scratches on a surface
-number of defective parts among 1000 tested
-number of transmitted bits received error
Examples of continuous random variables:
-electrical current
-length
-time

8. 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:

9. Example 2.2
Solution
Check whether the distribution is a probability distribution.
so the distribution is not a probability distribution. X 1 2 3 4
P(X=x)
0.125
0.375
0.025

10. Example 2.3 Check whether the function given by
can serve as the probability distribution of a discrete random variable.
Solution
Check whether the function given by

11. 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

12. Requirements for a probability density function of a continuous random variable X:

13. Example 2.4:
Consider the function
Find

14. Example 2.5 Let X be a continuous random variable with the
following probability density function

15. EXERCISE
A random variable x can assume 0,1,2,3,4. A probability distribution is shown here:
(a) Check whether this is probability distribution.
(b) Find
(c) Find X 1 2 3 4
P(X)
0.1
0.3 ?

16. 2. Let 3. Let

17. 2.4 CUMULATIVE DISTRIBUTION FUNCTION
The cumulative distribution function of a discrete random variable X , denoted as F(x), is
For a discrete random variable X, F(x) satisfies the following properties:
If the range of a random variable X consists of the values

18. The cumulative distribution function of a continuous random variable X is

19. Example 2.5
Solution x 1 2 3 4
f(x)
4/10
3/10
2/10
1/10
F(x)
7/10
9/10

20. Example 2.6
If X has the probability density

21. If X is a discrete random variable,
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
If X is a discrete random variable,
If X is a continuous random variable,

22. 2.5.2 Variance

23. 2.5.4 Properties of Expected Values
2.5.3 Standard Deviation
2.5.4 Properties of Expected Values
For any constant a and b,

24. 2.5.5 Properties of Variances
For any constant a and b,

25. Example 2.7 Find the mean, variance and standard deviation
of the probability function

26. Example 2.8 Let X be a continuous random variable with the
Following probability density function