chapter 5 discrete probability distributions files/statistics dr fatma/lect5.pdf · 2018. 7. 2. ·...

7- 1

Chapter 5

Discrete Probability Distributions

Seven edition - Elementary statistic

Bluman

7- 2

Outline

Introduction

Probability Distributions

Mean, Variance, Standard Deviation, and Expectation

The Binomial Distribution

Other Types of Distributions (Optional)

Summary

7- 3

Before probability distribution is defined formally, the

definition of a variable is reviewed. A variable was

defined as a characteristic or attribute that can assume

different values.

For example, if a die is rolled, a letter such as X can be

used to represent the outcomes. Then the value that X

can assume is 1, 2, 3, 4, 5, or 6, corresponding to the

outcomes of rolling a single die

7- 4

Also recall from Chapter 1 that you can classify

variables as discrete or continuous by observing the

values the variable can assume. If a variable can assume

only a specific number of values, such as the outcomes

for the roll of a die or the outcomes for the toss of a coin,

then the variable is called a discrete variable.

7- 5

o A discrete probability distribution consists of the

values a random variable can assume and the

corresponding probabilities of the values.

Rolling a Die

Construct a probability distribution for rolling a single die.

Solution

Since the sample space is 1, 2, 3, 4, 5, 6 and each outcome

has the same probability , the distribution is as shown.

7- 6

Outcome 1 2 3 4 5 6 sum

Probability

p(X)

1/6 1/6 1/6 1/6 1/6 1/6 1

Tossing Coins

Find the probability distribution for the variable x which

represent the number of head and represent graphically the

probability distribution for the sample space for tossing three

coins.

7- 7

Solution:

The values that X assumes are located on the x axis, and the values for P(X) are located on

the y axis

.

P(x)

Number of head 0 1 2 3 sum

Probability p(X) 1/8 3/8 3/8 1/8 1

3/83/8

1/81/8

x0 1 2 3

7- 8

Two Requirements for a Probability Distribution

7- 9

x 2 3 6

P(x) 1/2 1/3 1/6

7- 10

Mean, Variance, Standard Deviation

7- 11

7- 12

Outcome x 1 2 3 4 5 6

Probability P(x) 1/6 1/6 1/6 1/6 1/6 1/6

7- 13

Variance and Standard Deviation

7- 14

Remember that the variance and standard deviation

cannot be negative.

The standard deviation represent the square root of the

variance.

7- 15

Example

A box contains 5 balls. Two are numbered 3, one is numbered 4, and

two are numbered 5. The balls are mixed and one is selected at

random. After a ball is selected, its number is recorded. Then it is

replaced. If the experiment is repeated many times, find the variance

and standard deviation of the numbers on the balls.

Solution

Let X be the number on each ball. The probability distribution is

number of balls x 3 4 5

P(X) 2/5 1/5 2/5

7- 16

7- 17

Binomial distribution

A binomial experiment is a probability experiment that

satisfies the following four requirements:

1.There must be a fixed number of trials.

2.Each trial can have only two outcomes or outcomes

that can be reduced to two outcomes. These outcomes

can be considered as either success or failure.

3. The outcomes of each trial must be independent of

one another.

4.The probability of a success must remain the same for

each trial.

7- 18

Binomial Probability Formula

7- 19

Example

A coin is tossed 3 times. Find the probability of getting

exactly two heads.

Solution

This problem can be solved by looking at the sample space.

There are three ways to get two heads.

S={HHH, HHT, HTH, THH, TTH, THT, HTT, TTT }

So the answer will be =3/8 =0.375

Looking at the problem from the standpoint of a binomial

experiment, one can show that it meets the four

7- 20

requirements for the binomial distribution.

1.There are a fixed number of trials (three).

2.There are only two outcomes for each trial, heads or

tails.

3. The outcomes are independent of one another (the

outcome of one toss in no way affects the outcome of

another toss).

4.The probability of a success (heads) is in each case.

In this case, n =3, X =2, p=1/2 , and q=1/2 .

Hence, substituting in the formula gives before for binomial

7- 21

7- 22

7- 23

Example

7- 24

2- The Multinomial Distribution

Recall that in order for an experiment to be binomial, two

outcomes are required for each trial. But if each trial in an

experiment has more than two outcomes, a distribution

called the multinomial distribution must be used.

For example, a survey might require the responses of

“approve,” - “disapprove,” or “no opinion.”.

7- 25

7- 26

Example

7- 27

Multinomial distribution vs. Binomial distribution

The multinomial distribution is similar to the binomial

distribution but has the advantage of allowing you to

compute probabilities when there are more than two

outcomes for each trial in the experiment. That is, the

multinomial distribution is a general distribution, and the

binomial distribution is a special case of the multinomial

distribution

7- 28

The Poisson Distribution

Discrete probability distribution that is useful when n is

large and p is small and when the independent variables

occur over a period of time is called the Poisson

distribution.

The Poisson distribution can be used when a density of

items is distributed over a given area or volume, such as

the number of plants growing per acre.

7- 29

Formula for the Poisson Distribution

7- 30

Example

7- 31

7- 32

Summary of Discrete Distributions

Binomial distribution: used when there are only two

outcomes for a fixed number of independent trials and the

probability for each success remains the same for each

trial.

Multinomial distribution: used when the distribution has

more than two outcomes, the probabilities for each trial

remain constant, outcomes are independent, and there are a

fixed number of trials.

7- 33

Poisson distribution: used when n is large and p is

small, the independent variable occurs over a period of

time, or a density of items is distributed over a given area

or volume.

7- 34

Thanks