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1 Probability Models Grab a copy of the notes on the table by the door

2 Probability Models Bernoulli Trials Suppose a cereal manufacturer puts pictures of famous athletes in boxes of cereal, in the hope of increasing sales. The manufacturer announces that 20% of the boxes contains a picture of LeBron James, 30% a picture of Damica Patrick, and the rest a picture of Serena Williams. It’s possible to simulate the number of boxes we’d need to open to get one of each card. That’s a fairly complex question and one well suited for simulation. But many important questions can be answered more directly by using simple probability models. Stats: Modeling the World 4e 2

3 Probability Models Bernoulli Trials Searching for LeBron Suppose you’re a huge LeBron James fan. You don’t care about completely the whole sport card collection, but you’ve just got to have the LeBron’s picture. ‪ How many boxes do you expect you’ll have to open before you find him? This isn’t the same question we asked before, but this situation is simple enough for a probability model. Stats: Modeling the World 4e 3

4 Probability Models Bernoulli Trials Searching for LeBron We’ll keep the assumption that pictures are distributed at random and we’ll trust the manufacturer’s claim that 20% of the cards are Lebron. So, when you open the box, the probability that you succeed in finding Lebron is Stats: Modeling the World 4e 4

5 Probability Models Bernoulli Trials What are those outcomes for this example?Getting LeBron (success), or not (failure) Stats: Modeling the World 4e 5

6 Probability Models Bernoulli Trials Searching for LeBron Situations like this occur often and are called Bernoulli trials. Common examples of Bernoulli trials include tossing a coin, looking for defective products rolling off an assembly line, or even shooting free throws in basketball games. Just as we found equally like random digits to be the building blocks for our simulation, we can use Bernoulli trials to build a wide variety of useful probability models. Stats: Modeling the World 4e 6

7 Probability Models Bernoulli Trials Searching for LeBron Back to finding LeBron. We want to know how many boxes we’ll need to open to find his card. Let’s call this random variable Y = # boxes, and build a probability model for it. What’s the probability you find his picture in the first box of cereal? It’s 20%, of course. We could write P(Y = 1) = Why do you suppose Y = 1? Discuss with your partners Stats: Modeling the World 4e 7

8 Probability Models Bernoulli Trials Searching for LeBron How about the probability you don’t find LeBron until the second box? What does this mean? ‪ Well, that means you fail on the first trial and then succeed on the second. ‪ With the probability of success 20%, the probability of failure, denoted q, is 1 – 0.2 = 80%. ‪ Since the trials are independent, the probability of getting your first success on the second trial is P(Y = 2) = (0.8)(0.2) = 0.16 Stats: Modeling the World 4e 8

9 Probability Models Bernoulli Trials Searching for LeBron Stats: Modeling the World 4e Calvin and Hobbes © 1993 Bill Watterson courtesy of 9

10 Probability Models Bernoulli Trials Searching for LeBron Of course, you could have a run of “bad luck”. Maybe you won’t find LeBron until the fifth box of cereal. What are the chances of that? ‪ You’d have to fail 4 straight times and then succeed, so… ‪ P(Y = 5) = (0.8) 4 (0.2) = Stats: Modeling the World 4e 10

11 Probability Models Bernoulli Trials Stats: Modeling the World 4e 11

12 Probability Models 10% Rule One of the important requirement for Bernoulli trials is that the trials be independent. Sometimes that’s a reasonable assumption – when tossing a coin or rolling a die, for example. But that becomes a problem when (often!) we’re looking at situations involving samples chosen without replacement. We said that whether we find a LeBron James card in one box has no effect on the probabilities in other boxes. This is almost true. Stats: Modeling the World 4e 12

13 Probability Models 10% Rule Technically, if exactly 20% of the boxes had LeBron James cards, then when you find one, you’re reduced the number of remaining LeBron cards. With a few million boxes of cereal, though, the difference is hardly worth mentioning. But if you knew there were 2 LeBron James cards hiding in the 10 boxes of cereal on the market shelf, then finding one in the first box you would clearly change your chances of finding his picture in the next box. Stats: Modeling the World 4e 13

14 Probability Models 10% Rule If we had an infinite number of boxes, there wouldn’t be a problem. It’s selecting from a finite population that causes the probabilities to change, making the trials not independent. Obviously, taking 2 out of 10 boxes changes the probability. Taking even a few hundred out of millions, though, makes very little difference. Fortunately, it turns out that if we look at less than 10% of the population, we can pretend that the trials are independent and still calculate probabilities that are quite accurate. Stats: Modeling the World 4e 14

15 Probability Models 10% Rule That’s our 10% rule of thumb: ‪ The 10% Condition: Bernoulli trials must be independent. If that assumption is violated, it is still okay to proceed as long as we randomly sample fewer than 10% of the population. ‪ There is a formula that can adjust for even larger samples, called the finite population correction, but it’s beyond the scope of this course. Stats: Modeling the World 4e 15

16 Probability Models 1. Have your calculator ready 2. Grab a copy of the notes on the table by the door 3. Yesterday’s notes are on the table with the sharpener

17 Probability Models Bernoulli Trials & 10% Quick Review A trial is Bernoulli if… 1. There are two possible outcomes 2. The probability of success is constant 3. The trials are independent* What if the trial is not truly independent? So long as the random sample is smaller than 10% of the total population Stats: Modeling the World 4e 17

18 Probability Models The Geometric Model: Waiting for Success We want to model how long it will take to achieve the first success in a series of Bernoulli trials. The model tells us this probability is called the Geometric probability model. Geometric models are completely specified by one parameter, p, the probability of success, and are denoted by Geom(p). Since achieving the first success on trial number x required first experiencing x – 1 failures, the probabilities are expressed by a formula. Stats: Modeling the World 4e 18

19 Probability Models The Geometric Model: Waiting for Success Stats: Modeling the World 4e 19

20 Probability Models The Geometric Model: Waiting for Success Example: Spam and the Geometric Model Postini is a global company specializing in communications security. The company monitors over 1 billion Internet messages per day and recently reported that 91% of s are spam! Let’s assume that your is typical – 91% spam. We’ll also assume you aren’t using a spam filter, so every message gets dumped in your inbox (ugh!). And, since spam comes from many different sources, we’ll consider your messages to be independent. Question: Overnight your inbox collects . When you first check your in the morning, about how many spam s should you expect to have to wade through and discard before you find a real message? What’s the probability that the 4 th message in your inbox is the first one that isn’t spam? Stats: Modeling the World 4e 20

21 Probability Models The Geometric Model: Waiting for Success Stats: Modeling the World 4e 21

22 Probability Models The Geometric Model: Waiting for Success Stats: Modeling the World 4e 22

23 Probability Models The Geometric Model: Waiting for Success Step-by-Step: Working with a Geometric Model People with O-negative blood are called “universal donors” because O- negative blood can be given to anyone else, regardless of the recipient’s blood type. Only about 6% of people have O-negative blood. Questions: 1. If donors line up at random for a blood drive, how many do you expect to examine before you find someone who has O-negative blood? 2. What’s the probability that the first O-negative donor found is one of the first four people in line? Stats: Modeling the World 4e 23

24 Think Plan State the questions Check to see that these are Bernoulli trials. Variable Define the random variable Model Specify the model I want to estimate how many people I’ll need to check to find an O-negative donor, and the probability that 1 of the first 4 people is O-negative. There are two outcomes: success = O-negative and failure = other blood types The probability of success for each person is p = 0.06, because they are lined up randomly 10% Condition: Trials aren’t independent because the population is finite, but the donors lined up are fewer than 1-% of all possible donors. Let X = number of donors until one is O-negative I can model X with Geom(0.06) Probability Models The Geometric Model: Waiting for Success Stats: Modeling the World 4e 24

25 Show Mechanics Find the mean Calculate the probability of success on one of the first four trials. That’s the probability that X = 1, 2, 3, or 4 Probability Models The Geometric Model: Waiting for Success Stats: Modeling the World 4e 25

26 Tell Conclusion Interpret your results in context Blood drive such as this one expect to examine an average of 16.7 people to find a universal donor. About 22% of the time there will be one within the first 4 people in line. Probability Models The Geometric Model: Waiting for Success Stats: Modeling the World 4e 26

27 Probability Models The Geometric Model: Waiting for Success TI Tips: Finding Geometric Probabilities Your TI knows the geometric model. The commands to calculate probability distributions are found in the 2nd DISTR menu. Have a look. After many others (Yes, there’s still more to learn!) you’ll see two Geometric probability functions at the bottom of the list. Stats: Modeling the World 4e 27

28 Probability Models The Geometric Model: Waiting for Success TI Tips: Finding Geometric Probabilities geometpdf( The “pdf” stands for “probability density function.” This command allows you to find the probability of and individual outcome. You need only specify the p. which defines the Geometric model, and x, which indicates the number of trials until you get a success. The format is geometpdf(p,x). For example, suppose we want to know the probability that we find out first LeBron James picture in the fifth box of cereal. Since LeBron is in 20% of the boxes, we enter geometpdf(0.2,5) and hit ENTER. What does the calculator give us? Stats: Modeling the World 4e

29 Probability Models The Geometric Model: Waiting for Success TI Tips: Finding Geometric Probabilities geometcdf( The “cdf” stands for “cumulative density function,” meaning that it finds the sum of the probabilities of several possible outcomes. In general, the command geometcdf(p,x) calculates the probability of finding the first success on or before the x th trial. Let’s find the probability of getting a LeBron James picture by the time we open the fourth box of cereal – in other words, the probability our first success comes on the first box, or the second, or the third, or the fourth. Again we specify p = 0.2, and now x = 4. So, using geometcdf(.2,4) gives us a probability of Stats: Modeling the World 4e

30 Probability Models Geometric Model Quick Review A Geometric model is used for what purpose? Calculating the number of Bernoulli trials that need to be performed until the next success What are the two Geometric probability functions our calculators perform and what do they find? Geometpdf – “probability density function” Calculates the probability of when a first event occurs Geometcdf – “cumulative density function” Calculates the probability of an event occurring anywhere in the first x events. Stats: Modeling the World 4e 30

31 Probability Models The Binomial Model: Counting Success We can use the Bernoulli trials to answer other common questions. Suppose you buy 5 boxes of cereal. ‪ What’s the probability you get exactly 2 pictures of LeBron James? Before, we asked how long it would take until our first success. Now, we want to find the probability of getting 2 successes among the 5 trials. We are still talking about Bernoulli trials, but we’re asking a different question. Stats: Modeling the World 4e 31

32 Probability Models The Binomial Model: Counting Success This time we’re interested in the number of successes in the 5 trials, so we’ll call it X = number of successes. We want to find P(X = 2). ‪T‪This is an example of a Binomial probability ‪I‪It takes two parameters to define this Binomial model: the number of trials, n, and the probability of success, p ‪W‪We denote this model Binom(n, p). In this example, n = 5 trials, and p = 0.2, the probability of finding a LeBron James card in any trial. Stats: Modeling the World 4e 32

33 Probability Models The Binomial Model: Counting Success Exactly 2 successes in 5 trials means 2 successes and 3 failures. How do you suppose we might find this with what we already know? ‪ It seems logical that the probability should be (0.2) 2 (0.8) 3 ‪ Too bad: it’s not that easy. ‪ What does that calculation give you? ‪ That calculation would give you the probability of finding LeBron in the first 2 boxes and not in the next 3 – in that order. Stats: Modeling the World 4e 33

34 Probability Models The Binomial Model: Counting Success ‪ Couldn’t you also find LeBron in the third and fifth boxes and still have 2 successes? ‪ What would this probability calculation look like? (0.8)(0.8)(0.2)(0.8)(0.2) or (0.2) 2 (0.8) 3 ‪ Hmmm, the same result from two seemingly different situations. ‪ In fact, the probability will always be the same, no matter what order the successes and failures occur in. ‪ Anytime we get 2 successes in 5 trials, regardless of order, the probability will be (0.2) 2 (0.8) 2. Stats: Modeling the World 4e 34

35 Probability Models The Binomial Model: Counting Success ‪ What do you suppose we could do to find the answer to our exactly two LeBron cards question? ‪ We just need to count all the possible orders in which the outcomes can occur. Stats: Modeling the World 4e 35

36 Probability Models The Binomial Model: Counting Success ‪ That could potentially be a lot of work. ‪ Fortunately, these possible orders are disjoint ‪ (For example, if your two successes came on the first two trials, they couldn’t come on the last two.) ‪ So we could use the Addition Rule to add up the probabilities, but since they are all the same, we really only need to know how many orders are possible. ‪ For small n’s, we can just make a tree diagram and count the branches. Stats: Modeling the World 4e 36

37 Probability Models The Binomial Model: Counting Success Stats: Modeling the World 4e 37

38 Probability Models The Binomial Model: Counting Success Stats: Modeling the World 4e 38

39 Probability Models The Binomial Model: Counting Success Stats: Modeling the World 4e 39

40 Probability Models The Binomial Model: Counting Success It’s not hard to find the expected value for a binomial random variable. If we have 5 boxes, and LeBron’s picture is in 20% of them, then we would expect to have 5(0.2) = 1 success If we had 100 trials with probability of success 0.2, how many successes would you expect? ‪ Can you think of any reason not to say 20? ‪ It seems so simple that most people wouldn’t even stop to think about it. ‪ You just multiply the probability of success by n or… E(X) = np. Stats: Modeling the World 4e 40

41 Probability Models The Binomial Model: Counting Success Not fully convinced? A binomial model simply counts the number of successes in a series of n independent Bernoulli trials. ‪ Let Y = X 1 + X 2 + X 3 + … + X n ‪ E(Y) = E(X 1 + X 2 + X 3 + … + X n ) ‪ E(Y) = E(X 1 ) + E(X 2 ) + E(X 3 ) + … + E(X n ) ‪ E(Y) = p + p + p + … + p (there are n terms.) So, as we thought, the mean is E(Y) = np. Stats: Modeling the World 4e 41

42 Probability Models The Binomial Model: Counting Success Stats: Modeling the World 4e 42

43 Probability Models The Binomial Model: Counting Success For Example: Spam and the Binomial Model Recap: The communications monitoring company Postini has reported that 91% of messages are spam. Suppose your inbox contains 25 messages. Question: What are the mean and standard deviation of the number of real messages you should expect to find in your inbox? What’s the probability that you’ll find only 1 or 2 real messages? Stats: Modeling the World 4e 43

44 Probability Models The Binomial Model: Counting Success Stats: Modeling the World 4e 44

45 Probability Models The Binomial Model: Counting Success Stats: Modeling the World 4e 45

46 Probability Models The Binomial Model: Counting Success Step-by-Step: Working with a Geometric Model Suppose 20 donors come to a blood drive. Recall that 6% of people are “universal donors.” Questions: 1. What are the mean and standard deviation of the number of universal donors among them? 2. What is the probability that there are 2 or 3 universal donors? Stats: Modeling the World 4e 46

47 Think Plan State the questions Check to see that these are Bernoulli trials. Variable Define the random variable Model Specify the model I want to know the mean and standard deviation of the number of universal donors among 20 people and the probability that there are 2 or 3 of them. There are two outcomes: success = O-negative and failure = other blood types The probability of success for each person is p = 0.06, because they are lined up randomly 10% Condition: Trials aren’t independent because the population is finite, but the donors lined up are fewer than 1-% of all possible donors. Let X = number of donors until one is O-negative among n = 20 people. I can model X with Binom(20,0.06) Probability Models The Binomial Model: Counting Success Stats: Modeling the World 4e 47

48 Show Mechanics Find the expected value and standard deviation. Calculate the probability Probability Models The Binomial Model: Counting Success Stats: Modeling the World 4e 48

49 Tell Conclusion Interpret your results in context In groups of 20 randomly selected blood donors, I expect to find an average of 1.2 universal donors, with a standard deviation of About 31% of the time, I’d expect to find 2 or 3 universal donors among the 20 people. Probability Models The Binomial Model: Counting Success Stats: Modeling the World 4e 49

50 Probability Models The Geometric Model: Waiting for Success TI Tips: Finding Binomial Probabilities Remember how the calculator handles Geometric probabilities? Well, the commands for finding Binomial probabilities are essentially the same. Again, you’ll find them in the 2nd Distr menu. Stats: Modeling the World 4e 50

51 Probability Models The Geometric Model: Waiting for Success TI Tips: Finding Binomial Probabilities binompdf( This probability density function allows you to find the probability of an individual outcome. You need to define the Binomial model by specifying n and p, and then indicate the desired number of successes, x. The format is binompdf(n,p,X ). For example, recall that LeBron James’s picture is in 20% of the cereal boxes. Suppose that we want to know the probability of finding LeBron exactly twice among 5 boxes of cereal. We enter binompdf(5,0.2,2) then press ENTER and get… ‪ About a 20% chance of getting 2 LeBron pictures in 5 boxes of cereal. Stats: Modeling the World 4e 51

52 Probability Models The Geometric Model: Waiting for Success TI Tips: Finding Binomial Probabilities binomcdf( Need to add several Binomial probabilities? To find the total probability of getting x or fewer successes among n trials use the cumulative Binomial density function binomcfd(n,p,x). For example, suppose we have ten boxes of cereal and wonder about the probability of finding up to 4 pictures of LeBron. That’s the probability of 0, 1, 2, 3, or 4 successes, so using binomcdf(10,0.2,2) in the calculator we get… Pretty likely! Stats: Modeling the World 4e 52

53 Probability Models The Geometric Model: Waiting for Success TI Tips: Finding Binomial Probabilities Of course “up to 4” allows for the possibility that we end up with none. What’s the probability we get at least 4 pictures of LeBron in 10 boxes? Well, “at least 4” means “not 3 or fewer.” That’s the compliment of 0, 1, 2, or 3 successes. What would we type in the calculator to find this probability? ‪ 1 – binomcdf(10,0.2,3) ≈ ‪ There’s about a 12% chance we’ll find at least 4 pictures of LeBron in 10 boxes of cereal. Stats: Modeling the World 4e 53

54 Probability Models The Geometric Model: Waiting for Success  Just Checking The Pew Research Center reports that they are only able to contact 76% of randomly selected households drawn for telephone surveys. Suppose a pollster has a list of 12 calls to make. a) Why can these phone calls be considered Bernoulli trials. b) Find the probability that the fourth call is the first one that makes contact. c) Find the expected number of successful calls out of the 12. d) Find the standard deviation of the number of successful calls. e) Find the probability that exactly 9 of the 12 calls are successful. f) Find the probability that at least 9 of the calls are successful. Stats: Modeling the World 4e 54

55 Probability Models The Normal Model to the Rescue Suppose the Tennessee Red Cross anticipates the need for at least 1850 units of O-negative blood this year. It estimates that it will collect blood from 32,000 donors. How great is the risk that the Tennessee Red Cross will fall short of meeting its need? We’ve just learned how to calculate such probabilities. We can use the Binomial model with n = 32,000 and p = The probability of getting exactly 1850 units of O-negative blood from 32,000 donors is… Stats: Modeling the World 4e 55

56 Probability Models The Normal Model to the Rescue Stats: Modeling the World 4e 56

57 Probability Models The Normal Model to the Rescue Stats: Modeling the World 4e 57

58 Probability Models The Normal Model to the Rescue Stats: Modeling the World 4e 58

59 Probability Models The Normal Model to the Rescue Can we always use a Normal model to make estimates of Binomial probabilities? ‪ No. Consider the LeBron James situation – pictures in 20% of the cereal boxes. If we buy five boxes, the actual Binomial probabilities that we get 0, 1, 2, 3, 4, or 5 pictures of LeBron are 33%, 41%, 20%, 5%, 1%, and 0.03% respectively. The histogram at the right clearly shows this probability model is skewed, thus we should not try to estimate these probabilities by using a normal model. Stats: Modeling the World 4e 59

60 Probability Models The Normal Model to the Rescue Now suppose we open 50 boxes of this cereal and count the number of LeBron pictures we find. The histogram below shows this probability model. It is centered at np = 50(0.2) = 10 pictures, as expected. I appears to be fairly symmetric around that center. Stats: Modeling the World 4e 60

61 Probability Models The Normal Model to the Rescue This third histogram shows Binom(50,0.2) magnified somewhat and centered at the expected value of 10 pictures of LeBron. It looks close to Normal, for sure. With this larger sample size, it appears that a Normal model might be a useful approximation. Stats: Modeling the World 4e 61

62 Probability Models The Normal Model to the Rescue Stats: Modeling the World 4e 62


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