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Chapter 17: Geometric models AP Statistics B 1 Overview of Chapter 17 Two new models: Geometric model, and the Binomial model – Yes, the binomial model.

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Presentation on theme: "Chapter 17: Geometric models AP Statistics B 1 Overview of Chapter 17 Two new models: Geometric model, and the Binomial model – Yes, the binomial model."— Presentation transcript:


2 Chapter 17: Geometric models AP Statistics B 1

3 Overview of Chapter 17 Two new models: Geometric model, and the Binomial model – Yes, the binomial model involves Pascal’s triangles that (I hope) you learned about in Algebra 2 Use the geometric model whenever you want to find how many events you have to have before a “success” Use the binomial model to find out how many successes occur within a specific number of trials 2

4 Today’s coverage Introduction to the vocabulary: – Bernoulli trials – Geometric probability model – Binomial probability model Examples of both geometric and binomial probability models Nature of the geometric model (and review of series from Algebra 2) When to use the geometric model/practice on problems/solutions Finally, how to use the TI calculators to calculate probabilities by the geometric model 3

5 Vocabulary: Bernoulli trials Bernoulli trials – The only kind we do in Chapter 17 – Need to have definition firmly in mind – 3 requirements: 1.There are only two possible outcomes 2.Probability of success is constant (i.e., doesn’t change over time) 3.Trials are independent 4

6 Vocabulary: nomenclature for Bernoulli trials We’re going to start using “s” for success and “f” for failure (duh) Soon, however, we will switch to “p” for success and “q” for failure (don’t ask why….) Remember, remember, remember!— p+q=1 (s+f, too!) 5

7 Vocabulary: geometric and binomial models of probability Geometric probability model: – Counts the number of Bernoulli trials before the first success Binomial probability model: – Counts the number of successes in the first n trials (doesn’t have to be just one, as in the geometric model) 6

8 Examples: the geometric models Example: tossing a coin Success=heads; failure=tails COMPETELY ARBITRARY—in the examples we will reverse success and failure without any problems, so don’t get hung up on it Better way of thinking about it—binary, either/or 7

9 Examples: asking the geometry model question Typical question: What is the probability of not getting heads until the 5 th toss of the coin? Many geometric model questions are going to look like this: f f f f s (no success until 5 th toss) In terms of p and q, it looks like q q q q p We are talking sequences here! 8

10 Example: contrast geometric with the binomial model In the binomial model, we ask questions like “how many ways can we have exactly two successes in 5 Bernoulli trials? You would get a distribution like that on the right: 1.s s f f f 2.s f s f f 3.s f f s f 4.s f f f s 5.f s s f f 6.f s f s f 7.f s f f s 8.f f s s f 9.f f s f s 10.f f f s s 9

11 Example: binomial model using p and q instead of s and f An identical model to that of the last slide appears at the left This one, however, uses the p (success) and q (failure) that the textbook uses The patterns, however, are identical 1.p p q q q 2.p q p q q 3.p q q p q 4.p q q q p 5.q p p q q 6.q p q p q 7.q p q q p 8.q q p p q 9.q q p q p 10.q q q p p 10

12 Examples: geometric v. binomial Today Geometric models, tomorrow, Binomial The Geometric model is somewhat easier to follow The Binomial Model requires quite a bit more math Tomorrow, I’m going to show you a lecture by Arthur Benjamin on binomial math (½ hour) – Professor of Math, Harvey Mudd College (Claremont Colleges) – Good instructor, makes my jokes look less corny – Irksome mannerisms, but great content 11

13 Nature of the geometric model: first example (tossing a coin) Let’s start with flipping coins What is success? Let’s define it as getting heads as a result (p) So tails is q Probabilities? p=0.5 q=0.5 12

14 Nature of the geometric model: framing the question Q: What are the chances of not getting heads until the 4 th toss? 13

15 Nature of the geometric model: doing the calculations 1.Probability for failure is q, or q 3 for 3 successive failures (i.e., not getting heads until the 4 th toss) 2.Probability for success on 4 th try is p 3.Total probability is therefore q 3 p 4.Replace with numbers: (0.5) 3 ×(0.5)=(0.125)(0.5)=

16 Nature of the geometric model: the formulas (formulae for you pedants) Unfortunately, to derive most of the formulas we use, you have to use calculus This will be one of the few times where you’re simply going to have to memorize the equations (at least until you get to college and take calculus!) Sorry, sorry, sorry! 15

17 Nature of the geometric model: are we there yet? In other words, how many trials do we need until we succeed? Using p and q nomenclature, where x=number of trials until the first success occurs: P(X=x) = q x-1 p Remember our coin-tossing model: 4 times until we got heads (fill in the equation) You will use this a lot to calculate probabilities! 16

18 Nature of the geometric model: the mean and standard deviation Aka expected value, which equals E(X) μ=1/p, where p=probability of success Standard deviation Sadly, you just gotta memorize these! 17

19 Nature of the geometric model: summary 1.P(X=x) = q x-1 p, where x= number of trials before first success

20 Practice: Exercise 7 Basketball player makes 80% of his shots. Let’s set things up before we start. p=0.8, so q=0.2 (he makes 80% of his shots and misses 20%) Don’t calculate the mean just yet, because I’m going to show you that the definition of “success” often changes in the middle of the question! 19

21 Practice: Exercise 7(a) Misses for the first time on his 5 th attempt Use the probability model, except notice something really, really bizarre: the 5 th attempt appears to be a failure! That’s right, a failure!!! But it’s considered to be the “success”, so we have to reverse things 20

22 Practice, Exercise 7(a): setting up the calculation P(X=x) = q x-1 p is the formula. Here, this translates as (.8) 4 × 0.2 – Yes, I **know** it’s bizarre looking at the success as a failure, but hey….. Multiply this out on your calculators, and you should get… ? Everybody get that? Books says , or about 8.2% of the time will he not miss until the fifth shot 21

23 Practice, Exercise 7(a): lessons You can interchange failure for success in the probability model without problems You have to read the problem VERY carefully and not simply apply a formula. Had you done so here, and raised the MISSED basket to the 4 th power, you would have gotten a completely wrong answer “Failure” depends on context! What normally seems like “failure” (i.e., not making a basket) can be defined as success. “Binary” would probably be a better term than success and failure (you heard it here, first) 22

24 Practice, Exercise 7(b): a more normal set-up Q: “he makes his first basket on his fourth shot.” Except for reversing p and q, it’s the same as (a): – P(X=x) = q x-1 p is the formula. – P(miss 3 baskets before success)= (.2) 3 × 0.8= Very straightforward 23

25 Practice, Exercise 7(c): a trick you need to learn Question (c): “makes his first basket on one of his first three shots.” Here, we need to make a chart of all possibilities that fit the configuration (p=success/made basket, q=failure/missed): pqqqpqppp ppqqpp pqpqqp 24

26 Practice, Exercise 7(c): the long way On the right is a chart of all 7 possibilities With each possibility is the percent of the time it happens It al adds up to All these had to be assembled by hand applying the formulae in (a) and (b) ConfigurationProbability pqq0.032 ppq0.128 pqp0.128 ppp0.512 qpq0.128 qpp0.128 qqp

27 Practice, Exercise 7(c): the easy way If you have 2 possible outcomes and 3 trials, you will have 2 3 possible combinations We could get the 7 of 8 that we did in the previous slide Or, we can be clever: getting at least one basket in your first three shots is the complement of getting NO baskets in your first three shots, i.e., having three misses. 26

28 Practice, Exercise 7(c): the easy way/calculations So P(X)=1-failure to get any baskets in first three shots This equals 1-(0.2) 3 = =0.992 Which would you rather have in YOUR wallet? (oops….sorry, wrong commercial)…which would you rather spend your time on? 27

29 Practice, Exercise 9: “expected number of shots until miss” This is really a reading problem….what does “expected number of shots until misses” mean? It means, if you will excuse an unintentional pun, the mean, which equals 1/p. Now, the only question is, what’s p? Here, the “success” is missing. So the mean is 1/0.2 = 5. 28

30 Practice, Exercise 11: the AB blood problem NB: your instructor has AB+ blood. The Red Cross is always VERY glad to see me of all people have AB blood (we’re a rare breed) This problem will involve finding the mean as well as doing the probability calculations 29

31 Practice, Exercise 11(a): using the mean Q: On average, how many donors must be checked to find someone with Type AB blood? Classic case (“on average” is a clue!) of using the mean. Mean is 1/p = 1/0.04 = 25 30

32 Practice, Exercise 11(b): the easy way Q: What’s the probability that there is a Type AB donor among the first 5 people checked? Problem: there are 32 possible outcomes! (2 5 ) So let’s be clever (again) This is the same as asking what’s the probability of getting NO AB donors in the first 5? That’s equal to (0.96) 5 = Subtract that answer from one for , which is the answer to the question. 31

33 Practice, Exercise 11(c): similar to (b) Asking “what’s the probability that the first AB donor will be found among the first 6 people” is the same thing as subtraction the probability of NO AB donors from 1. No AB donors is (0.96) 6 = Complement is 1 − =

34 Practice, Exercise 11(d): Q: what’s the probability that we won’t find an AB donor before the 10 th person? Similar to saying we won’t find any AB donors in the first NINE people That’s (0.96) 9 =

35 Homework for tomorrow Ch 17, problems 8, 10, 12, 13, and

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