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C H A P T E R

Discrete Probability Distributions Outline

Chapter 5 Discrete Probability Distributions

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5‐1 Probability Distributions 5‐2 Mean, Variance, Standard Deviation, and  p Expectation 5‐3 The Binomial Distribution

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C H A P T E R

Discrete Probability Distributions Objectives 1 2 3 4

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Construct a probability distribution for a random  variable. Find the mean, variance, standard deviation, and Find the mean, variance, standard deviation, and  expected value for a discrete random variable. Find the exact probability for X successes in n trials  of a binomial experiment. Find the mean, variance, and standard deviation for  the variable of a binomial distribution.

5.1 Probability Distributions 

A random variable is a variable whose values are determined by chance.



A discrete probability distribution consists of the values a random variable can assume and the corresponding probabilities of the values values.



The sum of the probabilities of all events in a sample space add up to 1. Each probability is between 0 and 1, inclusively.

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Chapter 5 Discrete Probability Distributions

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Example 5-1: Rolling a Die Construct a probability distribution for rolling a single die.

Section 5-1 Example 5-1 Page #262

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Chapter 5 Discrete Probability Distributions

Example 5-2: Tossing Coins Represent graphically the probability distribution for the sample space for tossing three coins. .

Section 5-1 Example 5-2 Page #262

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 X PX 

Rounding Rule The mean, variance, and standard deviation should be rounded to one more decimal place than the outcome X.

VARIANCE:

 2    X 2  P  X     2

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Mean, Variance, Standard Deviation, and Expectation

5-2 Mean, Variance, Standard Deviation, and Expectation MEAN:  

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When fractions are used, they should be reduced to lowest terms.

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Chapter 5 Discrete Probability Distributions

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Example 5-5: Rolling a Die Find the mean of the number of spots that appear when a die is tossed. .

   X PX 

Section 5-2 Example 5-5 Page #268

 1  16  2  16  3  16  4  16  5  16  6  16  Bluman, Chapter 5

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 3.5 Bluman, Chapter 5

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Chapter 5 Discrete Probability Distributions

Section 5-2 Example 5-8 Page #269

Example 5-8: Trips of 5 Nights or More The probability distribution shown represents the number of trips of five nights or more that American adults take per year. (That is, 6% do not take any trips lasting five nights or more, 70% take one trip lasting five nights or more per year, etc.) t ) Find Fi d the th mean. .

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Example 5-8: Trips of 5 Nights or More

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Chapter 5 Discrete Probability Distributions

   X PX   0  0.06   1  0.70   2  0.20 

Section 5-2

 3  0.03   4  0.01

Example 5-9 Page #270

 1.2

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Example 5-9: Rolling a Die

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Chapter 5 Discrete Probability Distributions

Compute the variance and standard deviation for the probability distribution in Example 5–5. .

 2    X 2  P  X     2

Section 5-2

 2  12  16  2 2  16  3 2  16  4 2  16  5   6    3.5  2

 2  2.9 ,

1 6

2

1 6

Example 5-11 Page #271

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  1.7 Bluman, Chapter 5

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Example 5-11: On Hold for Talk Radio

Example 5-11: On Hold for Talk Radio

A talk radio station has four telephone lines. If the host is unable to talk (i.e., during a commercial) or is talking to a person, the other callers are placed on hold. When all lines are in use, others who are trying to call in get a busy signal. The probability th t 0, that 0 1, 1 2, 2 3, 3 or 4 people l will ill gett th through h iis shown in the distribution. Find the variance and standard deviation for the distribution.

  0  0.18   1  0.34   2  0.23   3  0.21 0 21  4  0.04 0 04   1.6 16

 2  0 2  0.18   12  0.34   2 2  0.23   3 2  0.21  4 2  0.04   1.6 

 2  1.2 , Bluman, Chapter 5

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  1.1

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Example 5-11: On Hold for Talk Radio

Example 5-11: On Hold for Talk Radio

A talk radio station has four telephone lines. If the host is unable to talk (i.e., during a commercial) or is talking to a person, the other callers are placed on hold. When all lines are in use, others who are trying to call in get a busy signal.

No, the four phone lines should be sufficient. The mean number of people calling at any one time is 1.6. Since the standard deviation is 1.1, most callers g four p phone would be accommodated byy having lines because µ + 2 would be 1.6 + 2(1.1) = 1.6 + 2.2 = 3.8. Very few callers would get a busy signal since at least 75% of the callers would either get through or be put on hold. (See Chebyshev’s theorem in Section 3–2.)

Should the station have considered getting more phone lines installed?

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Expectation 

The expected value, value or expectation expectation, of a discrete random variable of a probability distribution is the theoretical average of the variable.



The expected value is, by definition, the mean of the probability distribution.

E X    

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Chapter 5 Discrete Probability Distributions

Section 5-2 Example 5-13 Page #273

 X P X 

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Example 5-13: Special Die

5-3 The Binomial Distribution

A special six-sided die is made in which 3 sides have 6 spots, 2 sides have 4 spots, and 1 side has 1 spot.

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Many types of probability problems have only two possible outcomes or they can be reduced to two outcomes.

p value of the If the die is rolled,, find the expected number of spots that will occur.

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Examples E l iinclude: l d when h a coin i iis ttossed d it can land on heads or tails, when a baby is born it is either a boy or girl, etc.

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The Binomial Distribution

1. Each trial can have only two possible outcomes—success or failure. 2. There must be a fixed number of trials.

4. The probability of success must remain the same for each trial.

p

The numerical probability of success

q

Th numerical The i l probability b bilit off failure f il

n

The number of trials

X

The number of successes

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Section 5-3

or

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The symbol for the probability of failure

Chapter 5 Discrete Probability Distributions

n!  p X  q n X  n  X ! X !

number of possible desired outcomes

P(F)

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In a binomial experiment, the probability of exactly X successes in n trials is

n Cx 

The symbol for the probability of success

Note that X = 0, 1, 2, 3, ... , n

The Binomial Distribution

PX  

P(S)

P(S) = p and P(F) = 1 – p = q

3. The outcomes of each trial must be independent of each other.

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Notation for the Binomial Distribution

The binomial experiment is a probability experiment that satisfies these requirements:

PX  

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Example 5-16 Page #280

 p X  q n X   probability of a desired outcome

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Example 5-16: Survey on Doctor Visits A survey found that one out of five Americans say he or she has visited a doctor in any given month. If 10 people are selected at random, find the probability that exactly 3 will have visited a doctor last month. PX  

n!

 n  X ! X !

 p X  q n X

Section 5-3 Example 5-17 Page #281

n  10,"one out of five"  p  15 , X  3

P  3 

10!  1    7!3!  5 

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4     0.201 5

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Example 5-17: Survey on Employment A survey from Teenage Research Unlimited (Northbrook, Illinois) found that 30% of teenage consumers receive their spending money from part-time jobs. If 5 teenagers are selected at random, find the probability that at least 3 of them will have part-time jobs jobs. n  5, p  0.30,"at least 3"  X  3, 4,5 5! 3 2 P  3    0.30    0.70   0.132 P  X  3  0.132 2!3! 0.028 5! 4 1 P  4    0.30    0.70   0.028 0.002 1!4! 5! 5 0  0.162 P  5    0.30    0.70   0.002 0!5! Bluman, Chapter 5

Chapter 5 Discrete Probability Distributions

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Chapter 5 Discrete Probability Distributions

Section 5-3 Example 5-18 Page #281

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Example 5-18: Tossing Coins

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The Binomial Distribution

A coin is tossed 3 times. Find the probability of getting exactly two heads, using Table B.

The mean, variance, and standard deviation of a variable that has the binomial distribution can be found by using the following formulas.

n  3, p  12  0.5, X  2  P  2   0.375

Mean:   np p

Variance:  2  npq

Standard Deviation:   npq Bluman, Chapter 5

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Chapter 5 Discrete Probability Distributions

Section 5-3

Example 5-23: Likelihood of Twins The Statistical Bulletin published by Metropolitan Life Insurance Co. reported that 2% of all American births result in twins. If a random sample of 8000 births is taken, find the mean, variance, and standard deviation of the number of births that would result in twins twins.

  np  8000  0.02   160

Example 5-23 Page #284

 2  npq  8000  0.02  0.98   156.8  157

  npq  8000  0.02  0.98   12.5  13 Bluman, Chapter 5

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