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Chapter 5 Probability in our Daily Lives

Chapter 5 Probability in our Daily Lives. Section 5.1: How can Probability Quantify Randomness?. Learning Objectives. Random Phenomena Law of Large Numbers Probability Independent Trials Finding probabilities Types of Probabilities: Relative Frequency and Subjective.

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Chapter 5 Probability in our Daily Lives

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  1. Chapter 5Probability in our Daily Lives Section 5.1: How can Probability Quantify Randomness?

  2. Learning Objectives • Random Phenomena • Law of Large Numbers • Probability • Independent Trials • Finding probabilities • Types of Probabilities: Relative Frequency and Subjective

  3. Learning Objective 1:Random Phenomena • For random phenomena, the outcome is uncertain • In the short-run, the proportion of times that something happens is highly random • In the long-run, the proportion of times that something happens becomes very predictable Probability quantifies long-run randomness

  4. Learning Objective 2:Law of Large Numbers • As the number of trials increase, the proportion of occurrences of any given outcome approaches a particular number “in the long run” • For example, as one tosses a die, in the long run 1/6 of the observations will be a 6.

  5. Learning Objective 3:Probability • With random phenomena, the probability of a particular outcome is the proportion of times that the outcome would occur in a long run of observations • Example: • When rolling a die, the outcome of “6” has probability = 1/6. In other words, the proportion of times that a 6 would occur in a long run of observations is 1/6.

  6. Learning Objective 4:Independent Trials • Different trials of a random phenomenon are independent if the outcome of any one trial is not affected by the outcome of any other trial. • Example: • If you have 20 flips of a coin in a row that are “heads”, you are not “due” a “tail” - the probability of a tail on your next flip is still 1/2. The trial of flipping a coin is independent of previous flips.

  7. Learning Objective 5:How can we find Probabilities? • Calculate theoretical probabilities based on assumptions about the random phenomena. For example, it is often reasonable to assume that outcomes are equally likely such as when flipping a coin, or a rolling a die. • Observe many trials of the random phenomenon and use the sample proportion of the number of times the outcome occurs as its probability. This is merely an estimate of the actual probability.

  8. Learning Objective 6:Types of Probability:Relative Frequency vs. Subjective • The relative frequency definition of probability is the long run proportion of times that the outcome occurs in a very large number of trials - not always helpful/possible. • When a long run of trials is not feasible, you must rely on subjective information. In this case, the subjective definition of the probability of an outcome is your degree of belief that the outcome will occur based on the information available. • Bayesian statistics is a branch of statistics that uses subjective probability as its foundation

  9. Chapter 5: Probability in Our Daily Lives Section 5.2: How Can We Find Probabilities?

  10. Learning Objectives • Sample Space • Event • Probabilities for a sample space • Probability of an event • Basic rules for finding probabilities about a pair of events

  11. Learning Objectives • Probability of the union of two events • Probability of the intersection of two events

  12. Learning Objective 1:Sample Space • For a random phenomenon, the sample space is the set of all possible outcomes.

  13. Learning Objective 2:Event • An event is a subset of the sample space • An event corresponds to a particular outcome or a group of possible outcomes. • For example; • Event A = student answers all 3 questions correctly = (CCC) • Event B = student passes (at least 2 correct) = (CCI, CIC, ICC, CCC)

  14. Learning Objective 3:Probabilities for a sample space Each outcome in a sample space has a probability • The probability of each individual outcome is between 0 and 1 • The total of all the individual probabilities equals 1.

  15. Learning Objective 4:Probability of an Event • The probability of an event A, denoted by P(A), is obtained by adding the probabilities of the individual outcomes in the event. • When all the possible outcomes are equally likely:

  16. Learning Objective 4:Example: What are the Chances of being Audited? • What is the sample space for selecting a taxpayer? {(under $25,000, Yes), (under $25,000, No), ($25,000 - $49,000, Yes) …}

  17. Learning Objective 4:Example: What are the Chances of being Audited? For a randomly selected taxpayer in 2002, • What is the probability of an audit? • 310/80200=0.004 • What is the probability of an income of $100,000 or more? • 10700/80200=0.133 • What income level has the greatest probability of being audited? • $100,000 or more = 80/10700= 0.007

  18. Learning Objective 5:Basic rules for finding probabilities about a pair of events • Some events are expressed as the outcomes that • Are not in some other event (complement of the event) • Are in one event and in another event (intersection of two events) • Are in one event or in another event (union of two events)

  19. Learning Objective 5:Complement of an event • The complement of an event A consists of all outcomes in the sample space that are not in A. • The probabilities of A and of Ac add to 1 • P(Ac) = 1 – P(A)

  20. Learning Objective 5:Disjoint events • Two events, A and B, are disjoint if they do not have any common outcomes

  21. Learning Objective 5:Intersection of two events • The intersection of A and B consists of outcomes that are in both A and B

  22. Learning Objective 5:Union of two events • The union of A and B consists of outcomes that are in A or B or in both A and B.

  23. Learning Objective 6:Probability of the Union of Two Events • Addition Rule: • For the union of two events, • P(A or B) = P(A) + P(B) – P(A and B) • If the events are disjoint, P(A and B) = 0, so • P(A or B) = P(A) + P(B)

  24. Learning Objective 6:Example • 80.2 million tax payers (80,200 thousand) • Event A = being audited • Event B = income greater than $100,000 • P(A and B) = 80/80200=.001

  25. Learning Objective 7:Probability of the Intersection of Two Events Multiplication Rule: For the intersection of two independent events, A and B, P(A and B) = P(A) x P(B)

  26. Learning Objective 7:Example • What is the probability of getting 3 questionscorrect by guessing? • Probability of guessing correctly is .2 • What is the probability that a student answers at least 2 questions correctly? • P(CCC) + P(CCI) + P(CIC) + P(ICC) = • 0.008 + 3(0.032) = 0.104

  27. Learning Objective 7:Assuming independence • Don’t assume that events are independent unless you have given this assumption careful thought and it seems plausible.

  28. Learning Objective 7:Events Often Are Not Independent • Example: A Pop Quiz with 2 Multiple Choice Questions • Data giving the proportions for the actual responses of students in a class Outcome: II IC CI CC Probability: 0.26 0.11 0.05 0.58

  29. Learning Objective 7:Events Often Are Not Independent • Define the events A and B as follows: • A: {first question is answered correctly} • B: {second question is answered correctly} • P(A) = P{(CI), (CC)} = 0.05 + 0.58 = 0.63 • P(B) = P{(IC), (CC)} = 0.11 + 0.58 = 0.69 • P(A and B) = P{(CC)} = 0.58 • If A and B were independent, P(A and B) = P(A) x P(B) = 0.63 x 0.69 = 0.43 Thus, in this case, A and B are not independent!

  30. Chapter 5Probability in Our Daily Lives Section 5.3: Conditional Probability: What’s the Probability of A, Given B?

  31. Learning Objectives • Conditional probability • Multiplication rule for finding P(A and B) • Independent events defined using conditional probability

  32. Learning Objective 1:Conditional Probability • For events A and B, the conditional probability of event A, given that event B has occurred, is: • P(A|B) is read as “the probability of event A, given event B.” The vertical slash represents the word “given”. Of the times that B occurs, P(A|B) is the proportion of times that A also occurs

  33. Learning Objective 1:Conditional Probability

  34. Learning Objective 1:Example 1

  35. Learning Objective 1:Example 1

  36. Learning Objective 1:Example 1 • What was the probability of being audited, given that the income was ≥ $100,000? • Event A: Taxpayer is audited • Event B: Taxpayer’s income ≥ $100,000

  37. Learning Objective 1:Example 1 • What is the probability of being audited given that the income level is < $25,000 • Let A =Event being Audited • Let B = Income < $25,000 • P(A and B) = .0011 • P(B)=.1758 • .0011/.1758=.0063

  38. Learning Objective 1:Example 2 • A study of 5282 women aged 35 or over analyzed the Triple Blood Test to test its accuracy

  39. Learning Objective 1:Example 2 • A positive test result states that the condition is present • A negative test result states that the condition is not present • False Positive: Test states the condition is present, but it is actually absent • False Negative: Test states the condition is absent, but it is actually present

  40. Learning Objective 1:Example 2 • Assuming the sample is representative of the population, find the estimated probability of a positive test for a randomly chosen pregnant woman 35 years or older • P(POS) = 1355/5282 = 0.257

  41. Learning Objective 1:Example 2 • Given that the diagnostic test result is positive, find the estimated probability that Down syndrome truly is present • Summary: Of the women who tested positive, fewer than 4% actually had fetuses with Down syndrome

  42. Learning Objective 2:Multiplication Rule for Finding P(A and B) • For events A and B, the probability that A and B both occur equals: • P(A and B) = P(A|B) x P(B) also • P(A and B) = P(B|A) x P(A)

  43. Learning Objective 2:Example • Roger Federer – 2006 men’s champion in the Wimbledon tennis tournament • He made 56% of his first serves • He faulted on the first serve 44% of the time • Given that he made a fault with his first serve, he made a fault on his second serve only 2% of the time

  44. Learning Objective 2:Example • Assuming these are typical of his serving performance, when he serves, what is the probability that he makes a double fault? • P(F1) = 0.44 • P(F2|F1) = 0.02 • P(F1 and F2) = P(F2|F1) x P(F1) = 0.02 x 0.44 = 0.009

  45. Learning Objective 3:Independent Events Defined Using Conditional Probabilities • Two events A and B are independent if the probability that one occurs is not affected by whether or not the other event occurs • Events A and B are independent if: P(A|B) = P(A), or equivalently, P(B|A) = P(B) • If events A and B are independent, P(A and B) = P(A) x P(B)

  46. Learning Objective 3:Checking for Independence • To determine whether events A and B are independent: • Is P(A|B) = P(A)? • Is P(B|A) = P(B)? • Is P(A and B) = P(A) x P(B)? • If any of these is true, the others are also true and the events A and B are independent

  47. Learning Objective 3:Example • The diagnostic blood test for Down syndrome: POS = positive result NEG = negative result D = Down Syndrome DC = Unaffected

  48. Learning Objective 3:Example: Checking for Independent • Are the events POS and D independent or dependent? Is P(POS|D) = P(POS)? • P(POS|D) =P(POS and D)/P(D) = 0.009/0.010 = 0.90 • P(POS) = 0.257 • The events POS and D are dependent

  49. Chapter 5: Probability in Our Daily Lives Section 5.4: Applying the Probability Rules

  50. Learning Objectives • Is a “Coincidence” Truly an Unusual Event? • Probability Model • Probabilities and Diagnostic Testing • Simulation

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