2. Chapters 13 and 14 Probability and counting

3. Birthday Problem zWhat is the smallest number of people you need in a group so that the probability of 2 or more people having the same birthday is greater than 1/2? zAnswer: 23 No. of people23304060 Probability.507.706.891.994 We will solve this problem a few slides later using the laws of probability

4. Probability Formal study of uncertainty The engine that drives Statistics Primary objectives: 1.use the rules of probability to calculate appropriate measures of uncertainty. 2.Learn the probability basics so that we can do Statistical Inference

5. Introduction zNothing in life is certain zWe gauge the chances of successful outcomes in business, medicine, weather, and other everyday situations such as the lottery or the birthday problem

6. A phenomenon is random if individual outcomes are uncertain, but there is nonetheless a regular distribution of outcomes in a large number of repetitions. Randomness and probability Randomness ≠ chaos

7. Coin toss The result of any single coin toss is random. But the result over many tosses is predictable, as long as the trials are independent (i.e., the outcome of a new coin flip is not influenced by the result of the previous flip). First series of tosses Second series The probability of heads is 0.5 = the proportion of times you get heads in many repeated trials.

8. Approaches to Probability 1.Relative frequency event probability = x/n, where x=# of occurrences of event of interest, n=total # of observations yCoin, die tossing; nuclear power plants? zLimitations repeated observations not practical

9. Approaches to Probability (cont.) 2.Subjective probability individual assigns prob. based on personal experience, anecdotal evidence, etc. 3.Classical approach every possible outcome has equal probability (more later)

10. Basic Definitions zExperiment: act or process that leads to a single outcome that cannot be predicted with certainty zExamples: 1.Toss a coin 2.Draw 1 card from a standard deck of cards 3.Arrival time of flight from Atlanta to RDU

11. Basic Definitions (cont.) zSample space: all possible outcomes of an experiment. Denoted by S zEvent: any subset of the sample space S; typically denoted A, B, C, etc. Null event: the empty set  Certain event: S

12. Examples 1.Toss a coin once S = {H, T}; A = {H}, B = {T} 2.Toss a die once; count dots on upper face S = {1, 2, 3, 4, 5, 6} A=even # of dots on upper face={2, 4, 6} B=3 or fewer dots on upper face={1, 2, 3} 3.Select 1 card from a deck of 52 cards. S = {all 52 cards}

13. Laws of Probability

14. Coin Toss Example: S = {Head, Tail} Probability of heads = 0.5 Probability of tails = 0.5 3 ) The complement of any event A is the event that A does not occur, written as A. The complement rule states that the probability of an event not occurring is 1 minus the probability that is does occur. P(not A) = P(A) = 1 − P(A) Tail = not Tail = Head P(Tail ) = 1 − P(Head) = 0.5 Probability rules (cont’d) Venn diagram: Sample space made up of an event A and its complementary A, i.e., everything that is not A.

15. Birthday Problem zWhat is the smallest number of people you need in a group so that the probability of 2 or more people having the same birthday is greater than 1/2? zAnswer: 23 No. of people23304060 Probability.507.706.891.994

16. Example: Birthday Problem zA={at least 2 people in the group have a common birthday} zA’ = {no one has common birthday}

17. Unions: , or Intersections: , and A  A 

18. Mutually Exclusive (Disjoint) Events zMutually exclusive or disjoint events-no outcomes from S in common A and B disjoint: A  B=  A and B not disjoint A  A  Venn Diagrams

19. Addition Rule for Disjoint Events 4. If A and B are disjoint events, then P(A or B) = P(A) + P(B)

20. Laws of Probability (cont.) General Addition Rule 5. For any two events A and B P(A or B) = P(A) + P(B) – P(A and B)

21. 20 For any two events A and B P(A or B) = P(A) + P(B) - P(A and B) A B P(A) =6/13 P(B) =5/13 P(A and B) =3/13 A or B + _ P(A or B) = 8/13 General Addition Rule

22. Laws of Probability: Summary z1. 0  P(A)  1 for any event A z2. P(  ) = 0, P(S) = 1 z3. P(A’) = 1 – P(A) z4. If A and B are disjoint events, then P(A or B) = P(A) + P(B) z5. For any two events A and B, P(A or B) = P(A) + P(B) – P(A and B)

23. M&M candies ColorBrownRedYellowGreenOrangeBlue Probability0.30.2 0.1 ? If you draw an M&M candy at random from a bag, the candy will have one of six colors. The probability of drawing each color depends on the proportions manufactured, as described here : What is the probability that an M&M chosen at random is blue? What is the probability that a random M&M is any of red, yellow, or orange? S = {brown, red, yellow, green, orange, blue} P(S) = P(brown) + P(red) + P(yellow) + P(green) + P(orange) + P(blue) = 1 P(blue) = 1 – [P(brown) + P(red) + P(yellow) + P(green) + P(orange)] = 1 – [0.3 + 0.2 + 0.2 + 0.1 + 0.1] = 0.1 P(red or yellow or orange) = P(red) + P(yellow) + P(orange) = 0.2 + 0.2 + 0.1 = 0.5

24. Example: toss a fair die once S = {1, 2, 3, 4, 5, 6} zA = even # appears = {2, 4, 6} zB = 3 or fewer = {1, 2, 3}  P(A or  B) = P(A) + P(B) - P(A and  B) =P({2, 4, 6}) + P({1, 2, 3}) - P({2}) = 3/6 + 3/6 - 1/6 = 5/6

25. THE RELATIONSHIP BETWEEN ODDS AND PROBABILITIES World Series Odds The odds at the above link are the odds against a team winning the World Series, though the author claims they’re “odds for winning the World Series” Odds are frequently a source of confusion. Odds for? Odds against? From probability to odds From odds to probability

26. From Probability to Odds zIf event A has probability P(A), then the odds in favor of A are P(A) to 1-P(A). It follows that the odds against A are 1-P(A) to P(A) z If the probability of an earthquake in California is.25, then the odds in favor of an earthquake are.25 to.75 or 1 to 3. The odds against an earthquake are.75 to.25 or 3 to 1

27. From Odds to Probability zIf the odds in favor of an event E are a to b, then P(E)=a/(a+b) zin addition, P(E’)=b/(a+b) z If the odds in favor of UNC winning the NCAA’s are 3 (a) to 1 (b), then P(UNC wins)=3/4 P(UNC does not win)= 1/4

28. Probability Models The Equally Likely Approach (also called the Classical Approach)

29. Assigning Probabilities zIf an experiment has N outcomes, then each outcome has probability 1/N of occurring zIf an event A 1 has n 1 outcomes, then P(A 1 ) = n 1 /N

30. Dice You toss two dice. What is the probability of the outcomes summing to 5? There are 36 possible outcomes in S, all equally likely (given fair dice). Thus, the probability of any one of them is 1/36. P(the roll of two dice sums to 5) = P(1,4) + P(2,3) + P(3,2) + P(4,1) = 4 / 36 = 0.111 This is S: {(1,1), (1,2), (1,3), ……etc.}

31. We Need Efficient Methods for Counting Outcomes

32. Product Rule for Ordered Pairs zA student wishes to commute to a junior college for 2 years and then commute to a state college for 2 years. Within commuting distance there are 4 junior colleges and 3 state colleges. How many junior college-state college pairs are available to her?

33. Product Rule for Ordered Pairs zjunior colleges: 1, 2, 3, 4 zstate colleges a, b, c zpossible pairs: (1, a) (1, b) (1, c) (2, a) (2, b) (2, c) (3, a) (3, b) (3, c) (4, a) (4, b) (4, c)

34. Product Rule for Ordered Pairs zjunior colleges: 1, 2, 3, 4 zstate colleges a, b, c zpossible pairs: (1, a) (1, b) (1, c) (2, a) (2, b) (2, c) (3, a) (3, b) (3, c) (4, a) (4, b) (4, c) 4 junior colleges 3 state colleges total number of possible pairs = 4 x 3 = 12 4 junior colleges 3 state colleges total number of possible pairs = 4 x 3 = 12

35. Product Rule for Ordered Pairs zjunior colleges: 1, 2, 3, 4 zstate colleges a, b, c zpossible pairs: (1, a) (1, b) (1, c) (2, a) (2, b) (2, c) (3, a) (3, b) (3, c) (4, a) (4, b) (4, c) In general, if there are n 1 ways to choose the first element of the pair, and n 2 ways to choose the second element, then the number of possible pairs is n 1 n 2. Here n 1 = 4, n 2 = 3. In general, if there are n 1 ways to choose the first element of the pair, and n 2 ways to choose the second element, then the number of possible pairs is n 1 n 2. Here n 1 = 4, n 2 = 3.

36. Counting in “Either-Or” Situations NCAA Basketball Tournament: how many ways can the “bracket” be filled out? 1.How many games? 2.2 choices for each game 3.Number of ways to fill out the bracket: 2 63 = 9.2 × 10 18 Earth pop. about 6 billion; everyone fills out 1 million different brackets Chances of getting all games correct is about 1 in 1,000

37. Counting Example zPollsters minimize lead-in effect by rearranging the order of the questions on a survey zIf Gallup has a 5-question survey, how many different versions of the survey are required if all possible arrangements of the questions are included?

38. Solution zThere are 5 possible choices for the first question, 4 remaining questions for the second question, 3 choices for the third question, 2 choices for the fourth question, and 1 choice for the fifth question. zThe number of possible arrangements is therefore 5  4  3  2  1 = 120

39. Efficient Methods for Counting Outcomes zFactorial Notation: n!=1  2  …  n zExamples 1!=1; 2!=1  2=2; 3!= 1  2  3=6; 4!=24; 5!=120; zSpecial definition: 0!=1

40. Factorials with calculators and Excel zCalculator: non-graphing: x ! (second function) graphing: bottom p. 9 T I Calculator Commands (math button) zExcel: Insert function: Math and Trig category, FACT function

41. Factorial Examples z20! = 2.43 x 10 18 z1,000,000 seconds? zAbout 11.5 days z1,000,000,000 seconds? zAbout 31 years z31 years = 10 9 seconds z10 18 = 10 9 x 10 9 z20! is roughly the age of the universe in seconds

42. Permutations A B C D E zHow many ways can we choose 2 letters from the above 5, without replacement, when the order in which we choose the letters is important? z5  4 = 20

43. Permutations (cont.)

44. Permutations with calculator and Excel zCalculator non-graphing: nPr zGraphing p. 9 of T I Calculator Commands (math button) zExcel Insert function: Statistical, Permut

45. Combinations A B C D E zHow many ways can we choose 2 letters from the above 5, without replacement, when the order in which we choose the letters is not important? z5  4 = 20 when order important  Divide by 2: (5  4)/2 = 10 ways

46. Combinations (cont.)

47. ST 311 Powerball Lottery From the numbers 1 through 20, choose 6 different numbers. Write them on a piece of paper.

48. And the numbers are... z1616 z1111 z2z2 z1010 z8z8 z4z4 wow scream

49. Chances of Winning?

50. Example: Illinois State Lottery

51. North Carolina Powerball Lottery Prior to Jan. 1, 2009 After Jan. 1, 2009

52. The Forrest Gump Visualization of Your Lottery Chances zHow large is 195,249,054? z$1 bill and $100 bill both 6” in length z10,560 bills = 1 mile zLet’s start with 195,249,053 $1 bills and one $100 bill … z… and take a long walk, putting down bills end-to-end as we go

53. Raleigh to Ft. Lauderdale… … still plenty of bills remaining, so continue from …

54. … Ft. Lauderdale to San Diego … still plenty of bills remaining, so continue from…

55. … San Diego to Seattle

56. … still plenty of bills remaining, so continue from … … Seattle to New York

57. … still plenty of bills remaining, so … … New York back to Raleigh

58. Go around again! Lay a second path of bills Still have ~ 5,000 bills left!!

59. Chances of Winning NC Powerball Lottery? zRemember: one of the bills you put down is a $100 bill; all others are $1 bills. zPut on a blindfold and begin walking along the trail of bills. zYour chance of winning the lottery is the same as your chance of selecting the $100 bill if you stop at a random location along the trail and pick up a bill.

60. Virginia State Lottery

61. Probability Trees A Graphical Method for Complicated Probability Problems

62. Probability Tree Example: probability of playing professional baseball  6.1% of high school baseball players play college baseball. Of these, 9.4% will play professionally.  Unlike football and basketball, high school players can also go directly to professional baseball without playing in college…  studies have shown that given that a high school player does not compete in college, the probability he plays professionally is.002. Question 1: What is the probability that a high school baseball player ultimately plays professional baseball? Question 2: Given that a high school baseball player played professionally, what is the probability he played in college?

63. Question 1: What is the probability that a high school baseball player ultimately plays professional baseball? P(hs bb player plays professionally) =.061*.094 +.939*.002 =.005734 +.001878 =.007612.061*.094=.005734.939*.002=.001878

64. Question 2: Given that a high school baseball player played professionally, what is the probability he played in college?.061*.094=.005734.939*.002=.001878 P(hs bb player plays professionally) =.005734 +.001878 =.007612.061*.094=.005734

65. Example: AIDS Testing zV={person has HIV}; CDC: Pr(V)=.006 zP : test outcome is positive (test indicates HIV present) zN : test outcome is negative zclinical reliabilities for a new HIV test: 1.If a person has the virus, the test result will be positive with probability.999 2.If a person does not have the virus, the test result will be negative with probability.990

66. Question 1 zWhat is the probability that a randomly selected person will test positive?

67. Probability Tree Approach zA probability tree is a useful way to visualize this problem and to find the desired probability.

68. Probability Tree Multiply branch probs clinical reliability

69. Question 1: What is the probability that a randomly selected person will test positive?

70. Question 2 zIf your test comes back positive, what is the probability that you have HIV? (Remember: we know that if a person has the virus, the test result will be positive with probability.999; if a person does not have the virus, the test result will be negative with probability.990). zLooks very reliable

71. Question 2: If your test comes back positive, what is the probability that you have HIV?

72. Summary zQuestion 1: zPr(P ) =.00599 +.00994 =.01593 zQuestion 2: two sequences of branches lead to positive test; only 1 sequence represented people who have HIV. Pr(person has HIV given that test is positive) =.00599/(.00599+.00994) =.376

73. Recap zWe have a test with very high clinical reliabilities: 1.If a person has the virus, the test result will be positive with probability.999 2.If a person does not have the virus, the test result will be negative with probability.990 zBut we have extremely poor performance when the test is positive: Pr(person has HIV given that test is positive) =.376 zIn other words, 62.4% of the positives are false positives! Why? zWhen the characteristic the test is looking for is rare, most positives will be false.

74. examples 1. P(A)=.3, P(B)=.4; if A and B are mutually exclusive events, then P(A  B)=? A  B = , P(A  B) = 0 2. 15 entries in pie baking contest at state fair. Judge must determine 1 st, 2 nd, 3 rd place winners. How many ways can judge make the awards? 15 P 3 = 2730