chapter 5 discrete probability distributions files/statistics dr fatma/lect5.pdf · 2018. 7. 2. ·...
TRANSCRIPT
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Chapter 5
Discrete Probability Distributions
Seven edition - Elementary statistic
Bluman
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Outline
Introduction
Probability Distributions
Mean, Variance, Standard Deviation, and Expectation
The Binomial Distribution
Other Types of Distributions (Optional)
Summary
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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
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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.
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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.
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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.
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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
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Two Requirements for a Probability Distribution
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x 2 3 6
P(x) 1/2 1/3 1/6
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Mean, Variance, Standard Deviation
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Outcome x 1 2 3 4 5 6
Probability P(x) 1/6 1/6 1/6 1/6 1/6 1/6
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Variance and Standard Deviation
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Remember that the variance and standard deviation
cannot be negative.
The standard deviation represent the square root of the
variance.
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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
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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.
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Binomial Probability Formula
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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
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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
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Example
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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.”.
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Example
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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
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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.
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Formula for the Poisson Distribution
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Example
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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.
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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.
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Thanks