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Unit 2 Probability Distributions. Lesson 6 The Binomial Probability Distribution. To calculate a factorial on the TI-83, press , scroll over to the PRB menu, and choose 4: !. The Factorial Function.

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Slide1 l.jpg

Unit 2Probability Distributions

Lesson 6

The Binomial Probability Distribution


The factorial function l.jpg

To calculate a factorial on the TI-83, press , scroll over to the PRB menu, and choose 4: !

The Factorial Function

If n is a positive integer, n factorial, denoted n!, is the product of the integers from 1 to n, that is,

The quantity 0! is defined to be 1.


Example 1 computing factorials l.jpg

(d) scroll over to the

Example 1: Computing Factorials

Calculate:

(a) 5!

= 5 · 4 · 3· 2· 1

= 120

(b) 6!

= 6 ·5 · 4 · 3· 2· 1

= 6 · 5!

= 6 · 120 = 720

(c) 12!

= 479,001,600


Combinations rule l.jpg
Combinations Rule scroll over to the

Suppose you have a set of n distinctly different elements, and you wish to select a subset of x elements without regard to order.

This unordered arrangement is called a combination of n elements taken x at a time.


The combinations rule l.jpg

Pascal’s Triangle scroll over to the

To calculate a combination on the TI-83, press , scroll over to the PRB menu, and choose 3: nCr.

The Combinations Rule

The number of possible combinations of x elements chosen from a set of size n is denoted nCx.

The combinations rule states that


Example 2 computing with the combinations rule l.jpg
Example 2: Computing with the Combinations Rule scroll over to the

Find the numerical values of

(a) 6C3

(b) 100C98

(c) 50C50

(d) 50C0


Criteria for a binomial probability experiment l.jpg
Criteria for a Binomial Probability Experiment scroll over to the

An experiment is said to be a binomial experiment provided that

1. The experiment is performed a fixed number of times. Each repetition of the experiment is called a trial.

2. The trials are independent. This means the outcome of one trial will not affect the outcome of the other trials.

3. For each trial, there are two mutually exclusive outcomes, success (S) or failure (F).

4. The probability of success is fixed for each trial of the experiment.


Notation used in the binomial probability distribution l.jpg
Notation Used in the Binomial Probability Distribution scroll over to the

  • There are n independent trials of the experiment.

  • The letter p represents the probability of success, so that 1 – p is the probability of failure.

  • There are 2n possible outcomes.

  • The binomial random variable x is the number of successes observed in the n trials of the experiment, so x = 0, 1, 2, …, n

  • For each value of x, there are nCx outcomes (combinations) of x successes in n trials of the experiment.


Illustration constructing a binomial distribution l.jpg
Illustration: Constructing a Binomial Distribution scroll over to the

A public interest research group reports that only 20% of voting-age Floridians favor proposed legislation allowing casino-style gambling.

Let S represent an adult who supports the legislation and F represent one who opposes it. Thus, p = .20 and q = .80.

Four adults are randomly and independently selected, and each is asked whether they support the casino gambling bill.

Let x = the number of the four adults in the sample who support the bill.

Find the probability distribution for the binomial random variable x.


Slide10 l.jpg

x scroll over to the = 0

x = 1

x = 2

x = 3

x = 4

FFFF

SFFF

FSFF

FFSF

FFFS

FFSS

FSFS

FSSF

SFFS

SFSF

SSFF

FSSS

SFSS

SSFS

SSSF

SSSS

(.8)4 = .4096

4(.2)(.8)3= .4096

6(.2)2(.8)2= .1536

4(.2)3(.8) = .0256

(.2)4 = .0016

The probability distribution for a binomial experiment with n = 4 and p = .2

Illustration: Constructing a Binomial Distribution

In table form, the probability distribution is given by:


Slide11 l.jpg

P scroll over to the (x)

.4096

.4096

.4

.3

.2

.1536

.1

.0256

.0016

x

0

1

2

3

4

Number of adults in the sample of 4 who favor casino-style gambling

Illustration: Constructing a Binomial Distribution

In the form of a probability histogram, the probability distribution is given by:


The binomial probability distribution function l.jpg
The Binomial Probability Distribution Function scroll over to the

The probability of obtaining x successes in n independent trials of a binomial experiment where the probability of success is p is given by

where x = 0, 1, 2, . . ., n, and


Binomial distribution function on the ti 83 l.jpg

Press scroll over to the

DISTRibution Menu

Binomial Distribution Function on the TI-83

The binomial distribution function on the TI-83 is

binompdf(n,p,x)

where n = the total number of trialsp = the probability of success on a single trialx = the number of successes (may be a list)


Example 3 computing binomial probabilities l.jpg
Example 3: Computing Binomial Probabilities scroll over to the

If x is a binomial random variable, compute P(x) for each of the following cases:

(a) n = 5, x = 1, p = .2

P(1) = binompdf(5,.2,1) = .410

(b) n = 4, x = 2, q = .4

P(2) = binompdf(4,.6,2) = .346

(c) n = 3, x = 0, p = .7

P(0) = binompdf(3,.7,0) = .027

(d) n = 5, x = 3, p = .1

P(3) = binompdf(5,.1,3) = .008


Cumulative binomial probabilities l.jpg

P scroll over to the (x)

x

0

1

2

k

Press

to access the DISTR menu.

Cumulative Binomial Probabilities

Many problems relating to binomial distributions require the repeated use to the probability formula to sum up the probabilities for consecutive values of x.

The cumulative binomial probability distribution function,

binomcdf(n,p,k)

calculates sums of binomial probabilities.


Slide16 l.jpg

Summary of Cumulative Probabilities scroll over to the

For the binomial random variable x = 0, 1, 2, 3, ... , n, the cumulative probabilities are summarized as follows:


Example 4 computing cumulative binomial probabilities l.jpg

scientific notation scroll over to the

= 4.5×10–4 = .00045

Example 4: Computing Cumulative Binomial Probabilities

If x is a binomial random variable, use the TI-83 to find the following probabilities:

(a) P(x ≤ 5) for n = 15, p = .6

P(x ≤ 5) = binomcdf(15,.6,5) = .034

(b) P(x > 1) for n = 5, p = .1

P(x > 1) = 1 – P(x ≤ 1) = 1 – binomcdf(5,.1,1) = .081

(c) P(x < 10) for n = 25, p = .7

P(x < 10) = P(x ≤ 9) =binomcdf(25,.7,9)

(d) P(x ≥ 10) for n = 15, p = .9

P(x ≥ 10) = 1 – P(x ≤ 9) = 1 – binomcdf(15,.9,9)= .998


Example 5 using the binomial probability distribution function l.jpg
Example 5: scroll over to the Using the Binomial Probability Distribution Function

According to the United States Census Bureau, 18.3% of all households have 3 or more cars.

(a) In a random sample of 20 households, what is the probability that exactly 5 have 3 or more cars?

The sample is a binomial experiment with n = 20 and p = .183

P(5) = 20C5 (.183)5 (.817)15

= (15504) (.0002052) (.04823)

= .153

The probability of the sample containing exactly 5 homes with three or more cars is .153.


Slide19 l.jpg

Example 5: scroll over to the Using the Binomial Probability Distribution Function

According to the United States Census Bureau, 18.3% of all households have 3 or more cars.

(b) In a random sample of 20 households, what is the probability that less than 4 have 3 or more cars?

P(x < 4) = P(0) + P(1) +P(2) +P(3)

= binomcdf(20,.183,3)

= .488

The probability of the sample containing fewer than 4 homes with three or more cars is .488.


Slide20 l.jpg

Example 5: scroll over to the Using the Binomial Probability Distribution Function

According to the United States Census Bureau, 18.3% of all households have 3 or more cars.

(c) In a random sample of 20 households, what is the probability that at least 4 have 3 or more cars?

P(x≥ 4) = 1 – P(x < 4)

= 1 – .488

= .512

The probability of the sample containing at least 4 homes with three or more cars is .512.


The mean and variance of a binomial random variable l.jpg
The Mean and Variance of a Binomial Random Variable scroll over to the

A binomial experiment with n independent trials and probability of success p will have a mean and standard deviation given by the formulas

mean

variance

standard deviation


Example 6 finding the mean and standard deviation l.jpg
Example 6: Finding the Mean and Standard Deviation scroll over to the

According to the United States Census Bureau, 18.3% of all households have 3 or more cars.

In a simple random sample of 400 households, determine the mean and standard deviation number of households that will have 3 or more cars.

The sample is a binomial experiment with n = 400 and p = .183

μx = np = (400)(.183) = 73.2 households with 3 or more cars.

The expected number of households in the samplewith 3+ cars is 73.

= 7.7 households with 3+ cars.


Binomial probability distributions l.jpg

p varies, n fixed scroll over to the

p = .05, .10, .15, … , .95; n = 10

Binomial Probability Distributions

The following animations illustrate how the shape of a binomial probability distribution changes for varying values of n and p.

Note that:

  • Values of p near 0 or 1 may result in skewed distributions.

  • Values of p near .5 result in symmetric, mound-shaped distributions.


Slide24 l.jpg

n varies, p fixed scroll over to the

n = 2, 4, 6 … , 32; p = ¼

Binomial Probability Distributions

The following animations illustrate how the shape of a binomial probability distribution changes for varying values of n and p.

Note that:

  • Small values of n may result in skewed distributions.

  • Large values of n result in symmetric, mound-shaped distributions.


Bell shaped binomial distributions l.jpg

n scroll over to the

200

160

Large sample size

120

80

40

0

p

0

0.2

0.4

0.6

0.8

1

1

Bell-Shaped Binomial Distributions

As the number of trials n in a binomial experiment increases, the probability distribution of the random variable x becomes bell-shaped.

As a general rule of thumb, if np(1 – p) > 10, then the probability distribution will be approximately bell-shaped.


Interpreting standard deviation l.jpg

  • Empirical Rule

  • np(1 – p)≥ 10

  • Probability that x is within one standard deviation of the mean

  • Approximately 68%

  • Probability that x is within two standard deviations of the mean

  • At least 75%

  • Approximately 95%

  • Probability that x is within three standard deviations of the mean

  • At least 89%

  • Approximately 100%

Interpreting Standard Deviation

Chebyshev’s Rule and the Empirical Rule can be applied to binomial distributions to make statements about the likelihood of the random variable falling within one, two, or three standard deviations of the mean.


Example 7 applying the empirical rule l.jpg
Example 7: Applying the Empirical Rule scroll over to the

According to the United States Census Bureau, in 2000, 18.3% of all households have 3 or more cars. Suppose a researcher selects a simple random sample of 400 households and found that 91 households had 3 or more cars.

(a) Is the this binomial distribution bell-shaped?

np(1 – p) = (400)(.183)(.817)

= 59.8

Since np(1 – p) > 10, the binomial distribution is bell-shaped.


Slide28 l.jpg

Example 7: Applying the Empirical Rule scroll over to the

According to the United States Census Bureau, in 2000, 18.3% of all households have 3 or more cars. Suppose a researcher selects a simple random sample of 400 households and found that 91 households had 3 or more cars.

(b) Use the mean and standard deviation to compute the z-score for the observed value of x = 91. Interpret the result.

The mean and standard deviation are μx = 73.2 and σx = 7.7.

A sample of 91 households with 3+ cars is more than two standard deviations above the mean.


Slide29 l.jpg

Example 7: Applying the Empirical Rule scroll over to the

According to the United States Census Bureau, in 2000, 18.3% of all households have 3 or more cars. Suppose a researcher selects a simple random sample of 400 households and found that 91 households had 3 or more cars.

(c) Is this result unusual if the percentage of households with 3 or more cars is still 18.3%?

By the Empirical Rule, only 2.5% of all observations will fall two standard deviations above the mean, so the result is unusual.