Unit 2: Probability and distributions Lecture 4: Binomial ... › ~tjl13 › s101 › slides ›...

Unit 2: Probability and distributionsLecture 4: Binomial distribution

Statistics 101

Thomas Leininger

May 24, 2013

Announcements

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2 Binary outcomes

3 Binomial distributionConsidering many scenariosThe binomial distributionAside: The birthday problemExpected value and variability of successesActivityNormal approximation to the binomial

Statistics 101

U2 - L4: Binomial distribution Thomas Leininger

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Statistics 101 (Thomas Leininger) U2 - L4: Binomial distribution May 24, 2013 2 / 28

Binary outcomes

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Binary outcomes

Milgram experiment

Stanley Milgram, a Yale Universitypsychologist, conducted a series ofexperiments on obedience toauthority starting in 1963.

Experimenter (E) orders the teacher(T), the subject of the experiment, togive severe electric shocks to alearner (L) each time the learneranswers a question incorrectly.

The learner is actually an actor, andthe electric shocks are not real, but aprerecorded sound is played eachtime the teacher administers anelectric shock.

Binary outcomes

Milgram experiment (cont.)

These experiments measured the willingness of studyparticipants to obey an authority figure who instructed them toperform acts that conflicted with their personal conscience.

Milgram found that about 65% of people would obey authorityand give such shocks, and only 35% refused.

Over the years, additional research suggested this number isapproximately consistent across communities and time.

Binary outcomes

Each person in Milgram’s experiment can be thought of as a trial.

A person is labeled a success if she refuses to administer asevere shock, and failure if she administers such shock.

Since only 35% of people refused to administer a shock,probability of success is p = 0.35.

When an individual trial has only two possible outcomes, it isalso called a Bernoulli random variable.

Binary outcomes

Binomial distribution

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Statistics 101

Binomial distribution Considering many scenarios

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Statistics 101

Suppose we randomly select four individuals to participate in this ex-periment. What is the probability that exactly 1 of them will refuse toadminister the shock?

Let’s call these people Allen (A), Brittany (B), Caroline (C), andDamian (D). Each one of the four scenarios below will satisfy thecondition of “exactly 1 of them refuses to administer the shock”:

Scenario 1:0.35

(A) refuse×

0.65(B) shock

×0.65

(C) shock×

0.65(D) shock

= 0.0961

Scenario 2:0.65

(A) shock×

0.35(B) refuse

×0.65

(C) shock×

0.65(D) shock

= 0.0961

Scenario 3:0.65

(A) shock×

0.65(B) shock

×0.35

(C) refuse×

0.65(D) shock

= 0.0961

Scenario 4:0.65

(A) shock×

0.65(B) shock

×0.65

(C) shock×

0.35(D) refuse

= 0.0961

The probability of exactly one 1 of 4 people refusing to administer theshock is the sum of all of these probabilities.

0.0961 + 0.0961 + 0.0961 + 0.0961 = 4 × 0.0961 = 0.3844

Scenario 1:0.35

(A) refuse×

0.65(B) shock

×0.65

(C) shock×

0.65(D) shock

= 0.0961

Scenario 2:0.65

(A) shock×

0.35(B) refuse

×0.65

(C) shock×

0.65(D) shock

= 0.0961

Scenario 3:0.65

(A) shock×

0.65(B) shock

×0.35

(C) refuse×

0.65(D) shock

= 0.0961

Scenario 4:0.65

(A) shock×

0.65(B) shock

×0.65

(C) shock×

0.35(D) refuse

= 0.0961

0.0961 + 0.0961 + 0.0961 + 0.0961 = 4 × 0.0961 = 0.3844

Scenario 1:0.35

(A) refuse×

0.65(B) shock

×0.65

(C) shock×

0.65(D) shock

= 0.0961

Scenario 2:0.65

(A) shock×

0.35(B) refuse

×0.65

(C) shock×

0.65(D) shock

= 0.0961

Scenario 3:0.65

(A) shock×

0.65(B) shock

×0.35

(C) refuse×

0.65(D) shock

= 0.0961

Scenario 4:0.65

(A) shock×

0.65(B) shock

×0.65

(C) shock×

0.35(D) refuse

= 0.0961

0.0961 + 0.0961 + 0.0961 + 0.0961 = 4 × 0.0961 = 0.3844

Scenario 1:0.35

(A) refuse×

0.65(B) shock

×0.65

(C) shock×

0.65(D) shock

= 0.0961

Scenario 2:0.65

(A) shock×

0.35(B) refuse

×0.65

(C) shock×

0.65(D) shock

= 0.0961

Scenario 3:0.65

(A) shock×

0.65(B) shock

×0.35

(C) refuse×

0.65(D) shock

= 0.0961

Scenario 4:0.65

(A) shock×

0.65(B) shock

×0.65

(C) shock×

0.35(D) refuse

= 0.0961

0.0961 + 0.0961 + 0.0961 + 0.0961 = 4 × 0.0961 = 0.3844

Scenario 1:0.35

(A) refuse×

0.65(B) shock

×0.65

(C) shock×

0.65(D) shock

= 0.0961

Scenario 2:0.65

(A) shock×

0.35(B) refuse

×0.65

(C) shock×

0.65(D) shock

= 0.0961

Scenario 3:0.65

(A) shock×

0.65(B) shock

×0.35

(C) refuse×

0.65(D) shock

= 0.0961

Scenario 4:0.65

(A) shock×

0.65(B) shock

×0.65

(C) shock×

0.35(D) refuse

= 0.0961

0.0961 + 0.0961 + 0.0961 + 0.0961 = 4 × 0.0961 = 0.3844

Scenario 1:0.35

(A) refuse×

0.65(B) shock

×0.65

(C) shock×

0.65(D) shock

= 0.0961

Scenario 2:0.65

(A) shock×

0.35(B) refuse

×0.65

(C) shock×

0.65(D) shock

= 0.0961

Scenario 3:0.65

(A) shock×

0.65(B) shock

×0.35

(C) refuse×

0.65(D) shock

= 0.0961

Scenario 4:0.65

(A) shock×

0.65(B) shock

×0.65

(C) shock×

0.35(D) refuse

= 0.0961

0.0961 + 0.0961 + 0.0961 + 0.0961 = 4 × 0.0961 = 0.3844

Scenario 1:0.35

(A) refuse×

0.65(B) shock

×0.65

(C) shock×

0.65(D) shock

= 0.0961

Scenario 2:0.65

(A) shock×

0.35(B) refuse

×0.65

(C) shock×

0.65(D) shock

= 0.0961

Scenario 3:0.65

(A) shock×

0.65(B) shock

×0.35

(C) refuse×

0.65(D) shock

= 0.0961

Scenario 4:0.65

(A) shock×

0.65(B) shock

×0.65

(C) shock×

0.35(D) refuse

= 0.0961

0.0961 + 0.0961 + 0.0961 + 0.0961 = 4 × 0.0961 = 0.3844

Binomial distribution The binomial distribution

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Statistics 101

The question from the prior slide asked for the probability of givennumber of successes, k, in a given number of trials, n, (k = 1 successin n = 4 trials), and we calculated this probability as

# of scenarios × P(single scenario)

# of scenarios: there is a less tedious way to figure this out, we’llget to that shortly...

P(single scenario) = pk (1 − p)(n−k) (probability of success to the power of

number of successes, probability of failure to the power of number of failures)

The Binomial distribution describes the probability of having exactly ksuccesses in n independent Bernouilli trials with probability ofsuccess p.

Counting the # of scenarios

Earlier we wrote out all possible scenarios that fit the condition ofexactly one person refusing to administer the shock. If n was largerand/or k was different than 1, for example, n = 9 and k = 2:

RRSSSSSSSSRRSSSSSSSSRRSSSSS

· · ·

SSRSSRSSS· · ·

SSSSSSSRR

writing out all possible scenarios would be incredibly tedious andprone to errors.

RRSSSSSSS

SRRSSSSSSSSRRSSSSS

· · ·

SSRSSRSSS· · ·

SSSSSSSRR

RRSSSSSSSSRRSSSSSS

SSRRSSSSS· · ·

SSRSSRSSS· · ·

SSSSSSSRR

RRSSSSSSSSRRSSSSSSSSRRSSSSS

· · ·

SSRSSRSSS· · ·

SSSSSSSRR

Calculating the # of scenarios

Choose functionThe choose function is useful for calculating the number of ways tochoose k successes in n trials.(

n!k!(n − k)!

k = 1, n = 4:(41

1!(4−1)! =4×3×2×1

1×(3×2×1) = 4

k = 2, n = 9:(92

2!(9−1)! =9×8×7!2×1×7! =

722 = 36

Note: You can also use R for these calculations:

> choose(9,2)

[1] 36

n!k!(n − k)!

k = 1, n = 4:(41

1!(4−1)! =4×3×2×1

1×(3×2×1) = 4

k = 2, n = 9:(92

2!(9−1)! =9×8×7!2×1×7! =

722 = 36

> choose(9,2)

[1] 36

n!k!(n − k)!

k = 1, n = 4:(41

1!(4−1)! =4×3×2×1

1×(3×2×1) = 4

k = 2, n = 9:(92

2!(9−1)! =9×8×7!2×1×7! =

722 = 36

> choose(9,2)

[1] 36

Binomial distribution (cont.)

Binomial probabilities

If p represents probability of success, (1 − p) represents probability offailure, n represents number of independent trials, and k representsnumber of successes

P(k successes in n trials) =(nk

)pk (1 − p)(n−k)

Question

Which of the following is not a condition that needs to be met for thebinomial distribution to be applicable?

(a) the trials must be independent

(b) the number of trials, n, must be fixed

(c) each trial outcome must be classified as a success or a failure

(d) the number of desired successes, k, must be greater than thenumber of trials

(e) the probability of success, p, must be the same for each trial

Question

Which of the following is not a condition that needs to be met for thebinomial distribution to be applicable?

(a) the trials must be independent

(b) the number of trials, n, must be fixed

(c) each trial outcome must be classified as a success or a failure

(d) the number of desired successes, k, must be greater than thenumber of trials

(e) the probability of success, p, must be the same for each trial

Question

A 2012 Gallup survey suggests that 26.2% of Americans are obese.Among a random sample of 10 Americans, what is the probability thatexactly 8 are obese?

(a) pretty high

(b) pretty low

Gallup: http:// www.gallup.com/ poll/ 160061/ obesity-rate-stable-2012.aspx , January 23, 2013.

Question

(a) pretty high

(b) pretty low

Gallup: http:// www.gallup.com/ poll/ 160061/ obesity-rate-stable-2012.aspx , January 23, 2013.

Question

(a) 0.2628 × 0.7382

)× 0.2628 × 0.7382

(c)(108

)× 0.2628 × 0.7382

(d)(108

)× 0.2622 × 0.7388

Question

(a) 0.2628 × 0.7382

)× 0.2628 × 0.7382

(c)(108

)× 0.2628 × 0.7382 = 45 × 0.2628 × 0.7382 = 0.0005

(d)(108

)× 0.2622 × 0.7388

Binomial distribution Aside: The birthday problem

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Statistics 101

What is the probability that 2 randomly chosen people share a birth-day?

Pretty low, 1365 ≈ 0.0027.

What is the probability that at least 2 people out of 366 people share abirthday?

Exactly 1!

Pretty low, 1365 ≈ 0.0027.

Exactly 1!

Pretty low, 1365 ≈ 0.0027.

Exactly 1!

Pretty low, 1365 ≈ 0.0027.

Exactly 1!

What is the probability that at least 2 people (1 match) out of 16 peopleshare a birthday?

Somewhat complicated to calculate, but we can think of it as thecomplement of the probability that there are no matches in 16 people.

P(no matches) =365 × 364 × · · · × 350

≈ 0.72

P(at least 1 match) ≈ 0.28

If we had 30 people, then P(at least 1 match) ≈ 0.71

P(no matches) =365 × 364 × · · · × 350

≈ 0.72

P(no matches) =365 × 364 × · · · × 350

≈ 0.72

P(no matches) =365 × 364 × · · · × 350

≈ 0.72

Binomial distribution Expected value and variability of successes

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Expected value

A 2012 Gallup survey suggests that 26.2% of Americans are obese.

Among a random sample of 100 Americans, how many would you ex-pect to be obese?

Easy enough, 100 × 0.262 = 26.2.

Or more formally, µ = np = 100 × 0.262 = 26.2.

But this doesn’t mean in every random sample of 100 peopleexactly 26.2 will be obese. In fact, that’s not even possible. Insome samples this value will be less, and in others more. Howmuch would we expect this value to vary?

Expected value

Easy enough, 100 × 0.262 = 26.2.

Expected value

Easy enough, 100 × 0.262 = 26.2.

Expected value

Easy enough, 100 × 0.262 = 26.2.

Expected value and its variability

Mean and standard deviation of binomial distribution

µ = np σ =√

np(1 − p)

Going back to the obesity rate:

σ =√

np(1 − p) =√

100 × 0.262 × 0.738 ≈ 4.4

We would expect 26.2 out of 100 randomly sampled American tobe obese, give or take 4.4.

Note: Mean and standard deviation of a binomial might not always be whole

numbers, and that is okay. These values represent what we would expect to see on

average.

µ = np σ =√

np(1 − p)

σ =√

np(1 − p) =√

100 × 0.262 × 0.738 ≈ 4.4

average.

µ = np σ =√

np(1 − p)

σ =√

np(1 − p) =√

100 × 0.262 × 0.738 ≈ 4.4

average.

Unusual observations

Using the notion that observations that are more than 2 standarddeviations away from the mean are considered unusual and the meanand the standard deviation we just computed, we can calculate arange for the plausible number of obese Americans in randomsamples of 100.

26.2 ± (2 × 4.4) = (17.4, 35)

Question

An August 2012 Gallup poll suggests that 13% of Americans thinkhome schooling provides an excellent education for children. Woulda random sample of 1,000 Americans where only 100 share this opin-ion be considered unusual?

(a) No (b) Yes

http:// www.gallup.com/ poll/ 156974/ private-schools-top-marks-educating-children.aspx

Question

(a) No (b) Yes

µ = np = 1, 000 × 0.13 = 130

σ =√

np(1 − p) =√

1, 000 × 0.13 × 0.87 ≈ 10.6

Question

(a) No (b) Yes

µ = np = 1, 000 × 0.13 = 130

σ =√

np(1 − p) =√

1, 000 × 0.13 × 0.87 ≈ 10.6

Method 1: Range of usual observations: 130 ± 2 × 10.6 = (108.8, 151.2)100 is outside this range, so would be considered unusual.

Question

(a) No (b) Yes

µ = np = 1, 000 × 0.13 = 130

σ =√

np(1 − p) =√

1, 000 × 0.13 × 0.87 ≈ 10.6

Method 1: Range of usual observations: 130 ± 2 × 10.6 = (108.8, 151.2)100 is outside this range, so would be considered unusual.

Method 2: Z-score of observation: Z = x−meanSD = 100−130

10.6 = −2.83100 is more than 2 SD below the mean, so would be consideredunusual.

Binomial distribution Activity

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Statistics 101

Application exercise:Shapes of binomial distributions

1 Let’s simulate a Binomial distribution with n = 10 and p = 0.5.1 Flip a penny 10 times and record the number of heads.2 Repeat step 1.3 Record your number of heads (from each trial) on the dotplot on

the board.

2 What happens when we change n or p?1 Go to StatKey: http:// lock5stat.com/ statkey/ . In the Sampling

Distributions category, select Proportion. Change the proportion(p) and sample size n and simulate new data.

In R:> binom_sim <- rbinom(100,10,0.8)

> barplot(table(binom_sim))

Application exercise:Shapes of binomial distributions

1 Let’s simulate a Binomial distribution with n = 10 and p = 0.5.1 Flip a penny 10 times and record the number of heads.2 Repeat step 1.3 Record your number of heads (from each trial) on the dotplot on

the board.

2 What happens when we change n or p?1 Go to StatKey: http:// lock5stat.com/ statkey/ . In the Sampling

Distributions category, select Proportion. Change the proportion(p) and sample size n and simulate new data.

In R:> binom_sim <- rbinom(100,10,0.8)

> barplot(table(binom_sim))

Binomial distribution Normal approximation to the binomial

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Statistics 101

Histograms of number of successes

Hollow histograms of samples from the binomial model where p = 0.10and n = 10, 30, 100, and 300. What happens as n increases?

n = 10

0 2 4 6

n = 30

0 2 4 6 8 10

n = 100

0 5 10 15 20

n = 300

10 20 30 40 50

Normal probability plots of number of successes

Normal probability plots of samples from the binomial model wherep = 0.10 and n = 10, 30, 100, and 300. What happens as n increases?

●●●

● ●

●●●●

●●

●●●

●●

●●●●●●●●●

●●●●

−2 −1 0 1 2

n = 10

●●

●●●●

●●

●●●

●●

●●●

●●

−2 −1 0 1 2

n = 30

●●●

●●●●

●●

●●●

●●●●

●●

−2 −1 0 1 2

n = 100

●●

●●●

●●

−2 −1 0 1 2

n = 300

Low large is large enough?

The sample size is considered large enough if the expected numberof successes and failures are both at least 10.

np ≥ 10 and n(1 − p) ≥ 10

We can rewrite this to say that n should satisfy:

n ≥ (10/p) and n ≥ 10/(1 − p)

Are these conditions satisfied in the Gallup poll example?

Low large is large enough?

The sample size is considered large enough if the expected numberof successes and failures are both at least 10.

np ≥ 10 and n(1 − p) ≥ 10

We can rewrite this to say that n should satisfy:

n ≥ (10/p) and n ≥ 10/(1 − p)

Are these conditions satisfied in the Gallup poll example?

Yes. 1000 × 0.13 = 130; 1000 × 0.87 = 870

Question

Below are four pairs of Binomial distribution parameters. Which distri-bution can be approximated by the normal distribution?

(a) n = 100, p = 0.95(b) n = 25, p = 0.45

(c) n = 150, p = 0.05

(d) n = 500, p = 0.015

Question

Below are four pairs of Binomial distribution parameters. Which distri-bution can be approximated by the normal distribution?

(a) n = 100, p = 0.95(b) n = 25, p = 0.45

(c) n = 150, p = 0.05

(d) n = 500, p = 0.015

An analysis of Facebook users

A recent study found that “Facebook users get more than they give”.For example:

40% of Facebook users in our sample made a friend request, but63% received at least one request

Users in our sample pressed the like button next to friends’content an average of 14 times, but had their content “liked” anaverage of 20 times

Users sent 9 personal messages, but received 12

12% of users tagged a friend in a photo, but 35% werethemselves tagged in a photo

Any guesses for how this pattern can be explained?

http:// www.pewinternet.org/ Reports/ 2012/ Facebook-users/ Summary.aspx

An analysis of Facebook users

A recent study found that “Facebook users get more than they give”.For example:

40% of Facebook users in our sample made a friend request, but63% received at least one request

Users in our sample pressed the like button next to friends’content an average of 14 times, but had their content “liked” anaverage of 20 times

Users sent 9 personal messages, but received 12

12% of users tagged a friend in a photo, but 35% werethemselves tagged in a photo

Any guesses for how this pattern can be explained?

Power users who contribute much more content than the typical user.

http:// www.pewinternet.org/ Reports/ 2012/ Facebook-users/ Summary.aspx

This study also found that approximately 25% of Facebook users areconsidered power users. The same study found that the average Face-book user has 245 friends. What is the probability that the averageFacebook user with 245 friends has 70 or more friends who would beconsidered power users?

We are given that n = 245, p = 0.25, and we are asked for theprobability P(K ≥ 70).

P(X ≥ 70) = P(K = 70 or K = 71 or K = 72 or · · · or K = 245)

= P(K = 70) + P(K = 71) + P(K = 72) + · · · + P(K = 245)

This seems like an awful lot of work...

= P(K = 70) + P(K = 71) + P(K = 72) + · · · + P(K = 245)

Normal approximation to the binomial

When the sample size is large enough, the binomial distribution withparameters n and p can be approximated by the normal model withparameters µ = np and σ =

√np(1 − p).

In the case of the Facebook power users, n = 245 and p = 0.25.

µ = 245 × 0.25 = 61.25 σ =√

245 × 0.25 × 0.75 = 6.78

Bin(n = 245, p = 0.25) ≈ N(µ = 61.25, σ = 6.78).

20 40 60 80 100

Bin(245,0.25)N(61.5,6.78)

Question

What is the probability that the average Facebook user with 245 friendshas 70 or more friends who would be considered power users?

(a) 0.0251

(b) 0.0985

(c) 0.1128

(d) 0.9015

Question

(a) 0.0251

(b) 0.0985

(c) 0.1128

(d) 0.9015

61.25 70

Question

(a) 0.0251

(b) 0.0985

(c) 0.1128

(d) 0.9015

61.25 70

Z =obs − mean

70 − 61.256.78

= 1.29

Question

(a) 0.0251

(b) 0.0985

(c) 0.1128

(d) 0.9015

61.25 70

Z =obs − mean

70 − 61.256.78

= 1.29

Second decimal place of ZZ 0.05 0.06 0.07 0.08 0.09

1.0 0.8531 0.8554 0.8577 0.8599 0.8621

1.1 0.8749 0.8770 0.8790 0.8810 0.8830

1.2 0.8944 0.8962 0.8980 0.8997 0.9015

Question

(a) 0.0251

(b) 0.0985

(c) 0.1128

(d) 0.9015

61.25 70

Z =obs − mean

70 − 61.256.78

= 1.29

P(Z > 1.29) = 1 − 0.9015 = 0.0985

Second decimal place of ZZ 0.05 0.06 0.07 0.08 0.09

1.0 0.8531 0.8554 0.8577 0.8599 0.8621

1.1 0.8749 0.8770 0.8790 0.8810 0.8830

1.2 0.8944 0.8962 0.8980 0.8997 0.9015

Unit 2: Probability and distributions Lecture 4: Binomial ... › ~tjl13 › s101 › slides ›...

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