# Chapter 7 Probability. Example of a random circumstance. Random Circumstance. What does probability mean?? Goals in this chapter

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1 Homework (due Wed, Oct 27) Chapter 7: #17, 27, 28 Announcements: Midterm exams keys on web. (For a few hours the answer to MC#1 was incorrect on Version A.) No grade disputes now. Will have a chance to do that in writing at end of quarter. Grades are curved at end of quarter, not now. Material gets harder from here on! Practice problems with answers will be posted on website. Chapter 7 Probability Today: 7.1 to 7.3, small part of 7.4 Fi Fri: Finish i h74 7.4, Skip Section 7.6 Mon: Section 7.7 and supplemental material on intuition and probability Random Circumstance A random circumstance is one in which the outcome is unpredictable. Could be unpredictable because: It isn't determined yet or We have incomplete knowledge Example of a random circumstance Sex of an unborn child is unpredictable, so it is a random circumstance. We can talk about the probability that a child will be a boy. Why is it unpredictable? Before conception: It isn't determined yet After conception: We have incomplete knowledge Goals in this chapter Understand what is meant by probability Assign probabilities to possible outcomes of random circumstances. Learn how to use probability wisely What does probability mean?? What does it mean to say: The probability of rain tomorrow is.3. The probability that a coin toss will land heads up is ½. The probability that humans will survive to the year 3000 is.8. Is the word probability interpreted the same way in all of these? 1

2 Two basic interpretations of probability (Summary box, p. 237) Interpretation 1: Relative frequency Used for repeatable circumstances The probability of an outcome is the proportion of time that outcome does or will happen in the long run. Interpretation 2: Personal probability (subjective) Most useful for one-time events The probability of an outcome is the degree to which an individual believes it will happen. Two methods for determining relative frequency probability 1. Make an assumption about the physical world or 2. Observe the relative frequency of an outcome over many repetitions. Repetitions can be: a. Over time,, such as how often a flight is late b. Over individuals, by measuring a representative sample from a larger population and observing the relative frequency of an outcome or category of interest, such as the probability that a randomly selected person has a certain gene. How relative frequency probabilities are determined, Method 1: Make an assumption about the physical world. Examples: Flip a coin, probability it lands heads = ½. We assume the coin is balanced in such a way that t it is equally likely to land on either side. Draw a card from a shuffled, regular deck of cards, probability of getting a heart = ¼ We assume all cards are equally likely to be drawn How relative frequency probabilities are determined, Method 2a (over time): Observe the relative frequency of an outcome over many repetitions (long run relative frequency) Probability that a flight will be on time: According to United Airline s website, the probability that Flight 436 from SNA to Chicago will be on time (within 14 minutes of the stated time) is Based on observing this particular flight over many, many days; it was on time on 90% of those days. The relative frequency on time =.9 How relative frequency probabilities are determined, Method 2b (over individuals): Measure a representative sample and observe the relative frequency of possible outcomes or categories for the sample Probability that an adult female in the US believes in life after death is about.789. [It s.72 for males] Based on a national survey that asked 517 women if they believed in life after death 408 said yes Relative frequency is 408/517 =.789 Note about methods 2a and 2b: Usually these are just estimates of the true probability, based on n repetitions or n people in the sample. So, they have an associated margin of error with them. Example: Probability that an adult female in the US believes in life after death =.789, based on n = 517 women. 1 Margin of error is 517 =

3 Personal Probability Especially useful for one-time only events The personal probability of an outcome is the degree to which an individual believes it will happen. Examples: What is the probability that you will get a B in this class? We can t base the answer on relative frequency! LA Times, 10/8/09, scientists have determined that the probability of the asteroid Apophis hitting the earth in 2036 is 1 in 250,000. In 2004, they thought the probability was.027 that it would hit earth in Expert opinion has been updated. Notes about personal probability Sometimes the individual is an expert, and combines subjective information with data and models, such as in assessing the probability bilit of a magnitude 7 or higher h earthquake in our area in the next 10 years. Sometime there is overlap in these methods, such as determining probability of rain tomorrow uses similar pasts. Clicker questions not for credit! The probability that the winning Daily 3 lottery number tomorrow evening will be 777 is 1/1000. Which interpretation is best? A. Rel. freq. based on physical assumption B. Rel. freq. based on long run over time C. Rel. freq. based on representative sample D. Personal probability Clicker questions not for credit! The probability that the SF Giants will win the World Series this year is.40. Which interpretation is best? A. Rel. freq. based on physical assumption B. Rel. freq. based on long run over time C. Rel. freq. based on representative sample D. Personal probability Clicker questions not for credit! The probability that the plaintiff in a medical malpractice suit will win is.29. (Based on an article in USA Weekend) Which interpretation is best? A. Rel. freq. based on physical assumption B. Rel. freq. based on long run over time C. Rel. freq. based on representative sample D. Personal probability Section 7.3: Probability definitions and relationships We will use 2 examples to illustrate: 1. Daily 3 lottery winning number. Outcome = 3 digit number, from 000 to Choice of 3 parking lots, you always try lot 1, then lot 2, then lot 3. Lot 1 works 30% of the time, you aren t late Lot 2 works 50% of the time, you are late Lot 3 always works, so you park there 20% of the time, and when you do, you are very late! 3

4 Definitions of Sample space and Simple event The sample space S for a random circumstance is the collection of unique, non-overlapping outcomes. A simple event is one outcome in the sample space. Ex 1: S = {000, 001, 002,, 999} Simple event: 659 There are 1000 simple events. Ex 2: S = {Lot 1, Lot 2, Lot 3} Simple event: Lot 2 There are 3 simple events. Definition and notation for an event Definition: An event is any subset of the sample space. (One or more simple events) Notation: A, B, C, etc. Ex 1: A = winning number begins with 00 A = {000, 001, 002, 003,, 009} B = all same digits = {000, 111,, 999} Ex 2: A = late for class = {Lot 2, Lot 3} Probability of Events: Notation and Rules (for all interpretations and methods) Notation: P(A) = probability of the event A Rules: Probabilities are always assigned to simple events such that these 2 rules must hold: 1. 0 P(A) 1 for each simple event A 2. The sum of probabilities of all simple events in the sample space is 1. The probability of any event is the sum of probabilities for the simple events that are part of it. Special Case: Assigning Probabilities to Equally Likely Simple Events P(A) = probability of the event A Remember, Conditions for Valid Probabilities: Each probability is between 0 and 1. The sum of the probabilities over all possible simple events is 1. Equally Likely Simple Events If there are k simple events in the sample space and they are all equally likely, then the probability of the occurrence of each one is 1/k. Example: California Daily 3 Lottery Random Circumstance: A three-digit winning lottery number is selected. Sample Space: {000, 001, 002, 003,..., 997, 998, 999}. There are 1000 simple events. Probabilities for Simple Event: Probability that any specific three-digit number is a winner is 1/1000. Physical assumption: all three-digit numbers are equally likely. Event A = last digit is a 9 = {009, 019,..., 999}. P(A) = 100/1000 = 1/10. Event B = three digits are all the same = {000, 111, 222, 333, 444, 555, 666, 777, 888, 999}. Since event B contains 10 events, P(B) = 10/1000 = 1/100. Example 2: Simple events are not equally likely Simple Event Probability Park in Lot 1.30 Park in Lot 2.50 Park in Lot 3.20 Event A = late for class = {Lot 2, Lot 3} P(A) = =.70 Note that these sum to 1 4

5 Probability in daily language: People often express probabilities as percents, proportions, probabilities: United flight 436 from SNA to Chicago is late 10 percent of fthe time. The proportion of time United flight 436 is late is.1. The probability that United flight 436 from SNA to Chicago will be late is.1. These are all equivalent. RELATIONSHIPS BETWEEN EVENTS Defined for events in the same random circumstance only: Complement of an event Mutually exclusive events = disjoint events Defined for events in the same or different random circumstances: Independent events Conditional events Definition and Rule 1 (apply to events in the same random circumstance): Definition: One event is the complement of another event if: They have no simple events in common, AND They cover all simple events Notation: The complement of A is A C RULE 1: P(A C ) = 1 P(A) Ex 2: Random circumstance = parking on one day A = late for class, A C = on time P(A) =.70, so P(A C ) = 1.70 =.30 Complementary Events, Continued Rule 1: P(A) + P(A C ) = 1 Example: Daily 3 Lottery A = player buying single ticket wins A C = player does not win P(A) = 1/1000 so P(A C ) = 999/1000 Example: On-time flights A = flight you are taking will be on time A C = flight will be late Suppose P(A) =.80, then P(A C ) = 1.80 =.20. Mutually Exclusive Events Two events are mutually exclusive, or equivalently disjoint, if they do not contain any of the same simple events (outcomes). (Applies in same random circumstance.) Example: Daily 3 Lottery A = all three digits are the same (000, 111, etc.) B = the number starts with 13 (130, 131, etc.) The events A and B are mutually exclusive (disjoint), but they are not complementary. (No overlap, but don t cover all possibilities.) Independent and Dependent Events Two events are independent of each other if knowing that one will occur (or has occurred) does not change the probability that the other occurs. Two events are dependent if knowing that one will occur (or has occurred) changes the probability that the other occurs. The definitions can apply either to events within the same random circumstance or to events from two separate random circumstances. 5

6 EXAMPLE OF INDEPENDENT EVENTS Events in the same random circumstance: Daily 3 lottery on the same draw A = first digit is 0 B = last digit is 9 Knowing first digit is 0, P(B) is still 1/10. Events in different random circumstances: Daily 3 lottery on different draws A = today s winning number is 191 B = tomorrow s winning number is 875 Knowing today s # was 191, P(B) is still 1/1000 Mutually exclusive or independent? If two events are mutually exclusive (disjoint), they cannot be independent: If disjoint, then knowing A occurs means P(B) = 0 In independent, knowing A occurs gives no knowledge of P(B) Example of mutually exclusive (disjoint): A = today s winning number is 191, B = today s winning number is 875 Example of independent: A = today s winning number is 191 B = tomorrow s winning number is 875 Conditional Probabilities The conditional probability of the event B, given that the event A will occur or has occurred, is the long-run relative frequency with which event B occurs when circumstances are such that A also occurs; written as P(B A). P(B) = unconditional probability event B occurs. P(B A) = probability of B given A = conditional probability event B occurs given that we know A has occurred or will occur. EXAMPLE OF CONDITIONAL PROBABILITY Random circumstance: Observe one randomly selected child A = child slept in darkness as infant [Use total column.] P(A) = 172/479 =.36 B = child did not develop myopia [Use total row] P(B) = 342/479 =.71 P(B A) = P(no myopia slept in dark) [Use darkness row] = 155/172 =.90 P(B) NOTES ABOUT CONDITIONAL PROBABILITY 1. P(B A) generally does not equal P(B). 2. P(B A) = P(B) only when A and B are independent events 3. In Chapter 6, we were actually testing if two types of events were independent. 4. Conditional probabilities are similar to row and column proportions (percents) in contingency tables. (Myopia example on previous page.) 6

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