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Lab 6
Binomial Experiments
What are examples of a binomial experiment?
• I shoot 50 free-throws. How many do I make?
• A mother has 7 babies. How many are girls?
• I flip a coin 25 times. How many are heads?
• I call 100 phone #s from the phone book. How many people pick up?
• Each trial results in adding 1 or adding 0 to the total
• The probability of “success” does not change from one trial to the next
Guess which ones are binomial experiments
• 150 people play roulette. How many of them win?
• We ask 10 people at random to do 20 pushups. How many of them can do it?
• We ask 10 at random people to do 20 pushups. How many total pushups get
done?
• 30 random people go to Baskin Robbins. How many order vanilla?
• Each trial results in adding 1 or adding 0 to the total
• The probability of “success” does not change from one trial to the next
Guess which ones are binomial experiments (part 2)
• 150 people play roulette. How much money do they win?
• 150 people play poker. How many of them leave the table having lost $$?
• We select 10 people at random and measure their height. What is the average
height?
• We select 10 people at random and measure their height. How many people
are over 5’ tall?
• Each trial results in adding 1 or adding 0 to the total
• The probability of “success” does not change from one trial to the next
Guess which ones are binomial experiments (part 3)
• 𝑛 students play a game of trying to thread 21 beads onto a stick.
How many of them will succeed in under 1 minute?
• Each trial results in adding 1 or adding 0 to the total
• The probability of “success” does not change from one trial to the next
That’s today’s experiment
Why do we like binomial experiments?
Because binomial experiments have a formula for calculating the
probability of getting a certain result:
𝑃 𝑋 = 𝑥 =
𝑛
𝑥
⋅ 𝑝𝑥
⋅ 1 − 𝑝 𝑛−𝑥
where…
𝒙 is the number of “successes” in the experiment
𝒏 is the number of trials in the experiment
𝒑 is the probability of success in each trial
What is 𝑛
𝑥
????
𝑃 𝑋 = 𝑥 =
𝑛
𝑥
⋅ 𝑝𝑥
⋅ 1 − 𝑝 𝑛−𝑥
𝑛
𝑥
=
𝑛!
𝑛 − 𝑥 ! ⋅ (𝑥!)
But if you don’t feel like calculating it the long way, there is a
function in your calculator to calculate it
What is 𝑛! ????
𝒏! = 𝑛 ⋅ 𝑛 − 1 ⋅ 𝑛 − 2 ⋅ … ⋅ 2 ⋅ 1
𝟓! = 5 ⋅ 4 ⋅ 3 ⋅ 2 ⋅ 1 = 120
𝟑! = 3 ⋅ 2 ⋅ 1 = 6
𝟗! = 9 ⋅ 8 ⋅ 7 ⋅ 6 ⋅ 5 ⋅ 4 ⋅ 3 ⋅ 2 ⋅ 1 = 362880
For example…
𝑛
𝑥
=
𝑛!
𝑛 − 𝑥 ! ⋅ (𝑥!)
Using the binomial formula
For example, if it’s true that a coin is fair, what is the probability
that I get 9 heads out of 10 flips?
where…
𝒙 is the number of “successes” in the experiment = 𝟗
𝒏 is the number of trials in the experiment = 𝟏𝟎
𝒑 is the probability of success in each trial = 𝟎. 𝟓
𝑃 𝑋 = 𝑥 =
𝑛
𝑥
⋅ 𝑝𝑥
⋅ 1 − 𝑝 𝑛−𝑥
𝑃 𝑋 = 9 =
10
9
⋅ 0.59
) ⋅ 1 − 0.5 10−9
= 0.009765625
Our binomial experiment
• 𝑛 students play a game of trying to thread 21 beads onto a stick.
How many of them will succeed in under 1 minute?
. RULES .
• Move one bead at a time
• Keep the stick vertical
Group Max number of beads moved in
any one attempt
Outcome (success or failure)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
HINT:
On this histogram 
Which values of 𝑥 are most
likely to occur?
𝒙
mean 𝑥 = 𝑛 ⋅ 𝑝
HINT: If 𝑥 refers to a Binomial(𝑛, 𝑝) random variable, then
𝑠𝑑 𝑥 = 𝑛 ⋅ 𝑝 ⋅ (1 − 𝑝)
You must replace 𝒏 and 𝒑
with VALUES and calculate.
What are our values for 𝒏
and 𝒑???
https://www.slideshare.net/AndersonBussing/lab6-251224855
HINT:
If our class value of 𝑥 was 17, we
would write = 17 in those fields
and the bar would highlight red
Draw this histogram (with
highlighted bar) include
numbers and everything
𝑃 𝑋 = 𝑥 =⋅
𝑛
𝑥
𝑝𝑥
⋅ 1 − 𝑝 𝑛−𝑥
You must plug our values for 𝒙, 𝒏, 𝒑 into the below formula.
Show your work.
where…
𝒙 is the number of “successes” in the experiment
𝒏 is the number of trials in the experiment
𝒑 is the probability of success in each trial
HINT:
If the 80% was the real proportion of people who can do the bead game
in less than 1 minute, where would we expect our class value for 𝑥 to fall
on the histogram we made?
In the tails of the histogram? In the center?
Now where did our class value actually fall? Did it match what we
expected?
HINT:
Based on our where our class’ number of successes 𝑥 fell on the
histogram, do you think the game should be made harder or easier or
kept the same? In order so that 80% of people can complete the task in
time (and thus 20% are unable to complete the task in time).
What can they do to make the game harder/easier?
QUESTIONS 8-9:
Raise your hand and ask me if you have doubts about these.

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Lab6

  • 2. What are examples of a binomial experiment? • I shoot 50 free-throws. How many do I make? • A mother has 7 babies. How many are girls? • I flip a coin 25 times. How many are heads? • I call 100 phone #s from the phone book. How many people pick up? • Each trial results in adding 1 or adding 0 to the total • The probability of “success” does not change from one trial to the next
  • 3. Guess which ones are binomial experiments • 150 people play roulette. How many of them win? • We ask 10 people at random to do 20 pushups. How many of them can do it? • We ask 10 at random people to do 20 pushups. How many total pushups get done? • 30 random people go to Baskin Robbins. How many order vanilla? • Each trial results in adding 1 or adding 0 to the total • The probability of “success” does not change from one trial to the next
  • 4. Guess which ones are binomial experiments (part 2) • 150 people play roulette. How much money do they win? • 150 people play poker. How many of them leave the table having lost $$? • We select 10 people at random and measure their height. What is the average height? • We select 10 people at random and measure their height. How many people are over 5’ tall? • Each trial results in adding 1 or adding 0 to the total • The probability of “success” does not change from one trial to the next
  • 5. Guess which ones are binomial experiments (part 3) • 𝑛 students play a game of trying to thread 21 beads onto a stick. How many of them will succeed in under 1 minute? • Each trial results in adding 1 or adding 0 to the total • The probability of “success” does not change from one trial to the next That’s today’s experiment
  • 6. Why do we like binomial experiments? Because binomial experiments have a formula for calculating the probability of getting a certain result: 𝑃 𝑋 = 𝑥 = 𝑛 𝑥 ⋅ 𝑝𝑥 ⋅ 1 − 𝑝 𝑛−𝑥 where… 𝒙 is the number of “successes” in the experiment 𝒏 is the number of trials in the experiment 𝒑 is the probability of success in each trial
  • 7. What is 𝑛 𝑥 ???? 𝑃 𝑋 = 𝑥 = 𝑛 𝑥 ⋅ 𝑝𝑥 ⋅ 1 − 𝑝 𝑛−𝑥 𝑛 𝑥 = 𝑛! 𝑛 − 𝑥 ! ⋅ (𝑥!) But if you don’t feel like calculating it the long way, there is a function in your calculator to calculate it
  • 8. What is 𝑛! ???? 𝒏! = 𝑛 ⋅ 𝑛 − 1 ⋅ 𝑛 − 2 ⋅ … ⋅ 2 ⋅ 1 𝟓! = 5 ⋅ 4 ⋅ 3 ⋅ 2 ⋅ 1 = 120 𝟑! = 3 ⋅ 2 ⋅ 1 = 6 𝟗! = 9 ⋅ 8 ⋅ 7 ⋅ 6 ⋅ 5 ⋅ 4 ⋅ 3 ⋅ 2 ⋅ 1 = 362880 For example… 𝑛 𝑥 = 𝑛! 𝑛 − 𝑥 ! ⋅ (𝑥!)
  • 9. Using the binomial formula For example, if it’s true that a coin is fair, what is the probability that I get 9 heads out of 10 flips? where… 𝒙 is the number of “successes” in the experiment = 𝟗 𝒏 is the number of trials in the experiment = 𝟏𝟎 𝒑 is the probability of success in each trial = 𝟎. 𝟓 𝑃 𝑋 = 𝑥 = 𝑛 𝑥 ⋅ 𝑝𝑥 ⋅ 1 − 𝑝 𝑛−𝑥 𝑃 𝑋 = 9 = 10 9 ⋅ 0.59 ) ⋅ 1 − 0.5 10−9 = 0.009765625
  • 10. Our binomial experiment • 𝑛 students play a game of trying to thread 21 beads onto a stick. How many of them will succeed in under 1 minute? . RULES . • Move one bead at a time • Keep the stick vertical
  • 11. Group Max number of beads moved in any one attempt Outcome (success or failure) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
  • 12.
  • 13.
  • 14. HINT: On this histogram  Which values of 𝑥 are most likely to occur? 𝒙
  • 15. mean 𝑥 = 𝑛 ⋅ 𝑝 HINT: If 𝑥 refers to a Binomial(𝑛, 𝑝) random variable, then 𝑠𝑑 𝑥 = 𝑛 ⋅ 𝑝 ⋅ (1 − 𝑝) You must replace 𝒏 and 𝒑 with VALUES and calculate. What are our values for 𝒏 and 𝒑???
  • 17. HINT: If our class value of 𝑥 was 17, we would write = 17 in those fields and the bar would highlight red Draw this histogram (with highlighted bar) include numbers and everything
  • 18. 𝑃 𝑋 = 𝑥 =⋅ 𝑛 𝑥 𝑝𝑥 ⋅ 1 − 𝑝 𝑛−𝑥 You must plug our values for 𝒙, 𝒏, 𝒑 into the below formula. Show your work. where… 𝒙 is the number of “successes” in the experiment 𝒏 is the number of trials in the experiment 𝒑 is the probability of success in each trial
  • 19. HINT: If the 80% was the real proportion of people who can do the bead game in less than 1 minute, where would we expect our class value for 𝑥 to fall on the histogram we made? In the tails of the histogram? In the center? Now where did our class value actually fall? Did it match what we expected?
  • 20. HINT: Based on our where our class’ number of successes 𝑥 fell on the histogram, do you think the game should be made harder or easier or kept the same? In order so that 80% of people can complete the task in time (and thus 20% are unable to complete the task in time). What can they do to make the game harder/easier?
  • 21. QUESTIONS 8-9: Raise your hand and ask me if you have doubts about these.