4. Assigning Probability How likely it is that a particular outcome will be the result of a random circumstance The Relative Frequency Interpretation of Probability In situations that we can imagine repeating many times, we define the probability of a specific outcome as the proportion of times it would occur over the long run -- called the relative frequency of that particular outcome.
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6. Determining the Relative Frequency (Probability) of an Outcome Method 1: Make an Assumption about the Physical World (there is no bias) A Simple Lottery Choose a three-digit number between 000 and 999. Player wins if his or her three-digit number is chosen. Suppose the 1000 possible 3-digit numbers (000, 001, 002, 999) are equally likely. In long run , a player should win about 1 out of 1000 times . Probability = 0.0001 of winning. This does not mean a player will win exactly once in every thousand plays.
7. Determining the Relative Frequency (Probability) of an Outcome Method 2: Observe the Relative Frequency of random circumstances The Probability of Lost Luggage “1 in 176 passengers on U.S. airline carriers will temporarily lose their luggage .” This number is based on data collected over the long run. So the probability that a randomly selected passenger on a U.S. carrier will temporarily lose luggage is 1/176 or about 0.006.
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9. Estimating Probabilities from Observed Categorical Data Assuming data are representative, the probability of a particular outcome is estimated to be the relative frequency (proportion) with which that outcome was observed. Approximate margin of error for the estimated probability is
10. Nightlights and Myopia Assuming these data are representative of a larger population, what is the approximate probability that someone from that population who sleeps with a nightlight in early childhood will develop some degree of myopia ? Note : 72 + 7 = 79 of the 232 nightlight users developed some degree of myopia. So we estimate the probability to be 79/232 = 0.34. This estimate is based on a sample of 232 people with a margin of error of about 0.066 (1/√232 = ±0.666)
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12. Probability Definitions and Relationships Sample space: collection of unique, nonoverlapping possible outcomes of a random circumstance. Simple event: one outcome in the sample space; a possible outcome of a random circumstance. Event: a collection of one or more simple events in the sample space; often written as A, B, C, and so on.
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14. Example: Probability of Simple Events 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 any specific three-digit number is a winner is 1/1000. Assume all three-digit numbers are equally likely. Event A = last digit is a 9 = {009,019, . . . ,999}. Since one out of ten numbers in set, P (A) = 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 .
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