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Probability and Statistics Chapter 3 Notes Section 3-1 I. Probability Experiments.

Probability and Statistics Chapter 3 Notes Section 3-1 I. Probability Experiments.

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Probability and Statistics Chapter 3 Notes Section 3-1 I. Probability Experiments.

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  1. Probability and StatisticsChapter 3 Notes Section 3-1 I. Probability Experiments. A. When weather forecasters say “There is a 90% chance of rain tomorrow”, or a doctor says “There is a 35% chance of a successful surgery”, they are stating the likelihood, or probability, that a specific event will occur. 1. Decisions such as “should you plan a picnic for tomorrow”, or “should you proceed with surgery” are almost always based on these probabilities. B. A probability experiment is an action, or trial, through which specific results (counts, measurements or responses) are obtained. 1. The result of a single trial in a probability experiment is an outcome. 2. The set of all possible outcomes of a probability experiment is the sample space. 3. An event is a subset of the sample space. a) An event may consist of one or more outcomes.

  2. Probability and StatisticsChapter 3 Notes Section 3-1 I. Probability Experiments. C. Simple Example of the use of the terms probability experiment, sample space, event, and outcome. 1. Probability experiment: Roll a six-sided die. 2. Sample Space: 1, 2, 3, 4, 5, 6 3. Event: Roll an even number, 2, 4, 6 4. Outcome: Roll a 2. Section 3-1 Example 1: A probability experiment consists of tossing a coin and then rolling a six- sided die. Determine the number of outcomes and identify the sample space. SOLUTION: There are two possible outcomes when tossing a coin; a head (H) or a tail (T). For each of these, there are six possible outcomes when rolling a die: 1, 2, 3, 4, 5, or 6. One way to list all the possible outcomes (the sample space) for actions occurring in a sequence is to use a tree diagram.

  3. Probability and StatisticsChapter 3 Notes Section 3-1 I. Probability Experiments. D. Events are often represented by uppercase letters, such as A, B, and C. 1. An event that consists of a single outcome is called a simple event. a) In Example 1, the event “tossing heads and rolling a 3” is a simple event and can be represented as A=H3 b) In contrast, the event “tossing heads and rolling an even number” is NOT simple because it consists of three possible outcomes: B=H2, H4, H6 Section 3-1 Example 2: Determine the number of outcomes in each event. Then decide whether each event is simple or not. 1) For quality control, you randomly select a machine part from a batch that has been manufactured that day. Event A is selecting a specific defective machine part. 2) You roll a six-sided die. Event B is rolling at least a 4.

  4. Probability and StatisticsChapter 3 Notes Section 3-1 I. Probability Experiments. SOLUTION: Event A is a simple event; you either pick that specific defective part or you don’t Event B is not a simple event; it has three possible outcomes. You could roll a 4, 5, or 6 and roll at least 4. You ask for a student’s age at his or her last birthday. Decide whether each event is simple or not. 1) Event C: The student’s age is between 15 and 18, inclusive. 2) Event D: The student’s age is 17. SOLUTION: Event C is not simple; the ages of 15, 16, 17 and 18 are all possible outcomes. Event D is simple: the only possible successful outcome is that the student is 17 years old.

  5. Probability and StatisticsChapter 3 Notes Section 3-1 II. The Fundamental Counting Principle A. If one event can occur in m ways and a second event can occur in n ways, the number of ways that the two events can occur in sequence is m*n. 1) This rule can be extended for any number of events occurring in sequence. B. In plain English, the number of ways that events can occur in sequence is found by multiplying the number of ways one event can occur by the number of ways the other event(s) can occur. Section 3-1 Example 3: You are purchasing a new car. The possible manufacturers, car sizes, and colors are listed. Manufacturer: Ford, GM, Honda Car size: Compact, midsize Color: White (W), red (R), black (B), and green (G) How many different ways can you select one manufacturer, one car size, and one color? Use a tree diagram to check your answer.

  6. Probability and StatisticsChapter 3 Notes Section 3-1 SOLUTION: There are 3 manufacturers, 2 sizes, and 4 colors, so the number of different ways is equal to 3*2*4, or 24.

  7. Probability and StatisticsChapter 3 Notes Section 3-1   Section 3-1 Example 4: The access code for a car’s security system consists of 4 digits. Each digit can be 0 through 9. 1) How many access codes are possible if each digit can be used only once, and not repeated? 2) How many access codes are possible if each digit can be repeated? 3) How many access codes are possible if each digit can be repeated, but the first digit cannot be 0 or 1?

  8. Probability and StatisticsChapter 3 Notes Section 3-1 SOLUTIONS: 1) There are 10 possible choices for the first digit (0, 1, 2, 3, 4, 5, 6, 7, 8, 9) There are 9 possible choices for the second digit (ten minus the one you used for the first digit) There are 8 possible choices for the third digit (nine minus the one you used for the second digit) There are 7 possible choices for the last digit (eight minus the one you used for the third digit) 10*9*8*7 = 5,040 possible codes. 2) There are 10 possible choices for the first digit (0, 1, 2, 3, 4, 5, 6, 7, 8, 9) There are 10 possible choices for the second digit There are 10 possible choices for the third digit There are 10 possible choices for the last digit 10*10*10*10 = 10,000 possible codes.

  9. Probability and StatisticsChapter 3 Notes Section 3-1 3) There are 8 possible choices for the first digit (2, 3, 4, 5, 6, 7, 8, 9) There are 10 possible choices for the second digit There are 10 possible choices for the third digit There are 10 possible choices for the last digit 8*10*10*10 = 8,000 possible codes.

  10. Probability and StatisticsChapter 3 Notes Section 3-1 III. Types of Probability A. Classical (or theoretical probability) is used when each outcome in a sample space is equally likely to occur. The classical probability for an event E is given by . Section 3-1 Example 5: You roll a six-sided die. Find the probability of each event. 1) Event A: rolling a 3 2) Event B: rolling a 7 3) Event C: rolling a number less than 5 SOLUTIONS: 1) The sample space consists of 6 outcomes: 1, 2, 3, 4, 5, 6 There is one outcome in Event A: A=3 So, P (rolling a 3) 2) The sample space consists of 6 outcomes: 1, 2, 3, 4, 5, 6 There are no outcomes in Event B: You cannot roll a 7 on a six-sided die So, P (rolling a 7)

  11. Probability and StatisticsChapter 3 Notes Section 3-1 SOLUTIONS: 3) The sample space consists of 6 outcomes: 1, 2, 3, 4, 5, 6 There are four outcomes in Event C: A=1, 2, 3, 4 So, P (rolling < 5) B. Empirical (or statistical) probability is used when each outcome of an event is not equally likely to occur. When an experiment is repeated many times, regular patterns are formed. These patterns make it possible to find empirical probability. Empirical (or statistical) probability) is based on observations obtained from probability experiments. The empirical probability of an event E is the relative frequency of event E. ; this can be expressed as

  12. Probability and StatisticsChapter 3 Notes Section 3-1 Section 3-1 Example 6: A company is conducting an online survey of randomly selected individuals to determine if traffic congestion is a problem in their community. So far, 320 people have responded to the survey. The frequency distribution in your notes shows the results. What is the probability that the next person that responds to the survey says that traffic congestion is a serious problem in their community? SOLUTION: The event is a response of “It is a serious problem “. The frequency of this event is 123. Because the total of the frequencies is 320, the empirical probability of the next person saying that traffic congestion is a serious problem in their community is:

  13. Probability and StatisticsChapter 3 Notes Section 3-1 C. Subjective probability: these result from intuition, educated guesses and estimates. They are not as trusted in statistical studies as theoretical or empirical probabilities are. D. Law of Large Numbers A. As an experiment is repeated over and over, the empirical probability of an event approaches the theoretical (actual) probability of an event. If you toss a fair coin 10 times, you may only get 3 heads. If you toss a fair coin 100,000 times, you will get close to 50,000 heads.

  14. Probability and StatisticsChapter 3 Notes Section 3-1 Section 3-1 Example 7: You survey a sample of 1000 employees at a large company and record the age of each. The results are shown in the frequency distribution in your notes. If you randomly select another employee, what is the probability that the employee will be between 25 and 34 years old? SOLUTION: The event is selecting an employee who is between 25 and 34 years old. In your survey, the frequency of this event is 366. Because the total of the frequencies is 1000, the probability of selecting an employee between the ages of 25 and 34 years old is:

  15. Probability and StatisticsChapter 3 Notes Section 3-1 Section 3-1 Example 8: Classify each statement as an example of classical probability, empirical probability, or subjective probability 1. The probability that you will be married by age 30 is 0.50. 2. The probability that a voter chosen at random will vote Republican is 0.45. 3. The probability of winning a 1000-ticket raffle with one ticket is SOLUTIONS 1. This is a subjective probability, based on a feeling or a guess. 2. This is an example of an empirical probability, since it most likely resulted from a survey or poll and the outcomes are not equally likely. 3. This is an example of classical (theoretical) probability. You know the number of outcomes, and each one is equally likely to occur.

  16. Probability and StatisticsChapter 3 Notes Section 3-1 E. Range of Probabilities Rule 1. Very important to remember this AT ALL TIMES!! ALL probabilities have a value between 0 and 1. An impossible event has a probability of 0. An event that is guaranteed to occur has a probability of 1. An event that has an equal chance of occurring or not occurring has a probability of 0.50. Any event with a probability of less than 0.05 or greater than 0.95 is considered to be unusual when it occurs. F. Complementary Events 1. Complementary events have probabilities that add up to 1. If there is a 76% chance of rain, and a 24% chance that it doesn’t rain, raining or not raining are complementary events. 2. Definition: The complement of event E is the set of all outcomes in a sample space that are not included in event E. The complement of event E is denoted by E’ and is read as “E prime”.

  17. Probability and StatisticsChapter 3 Notes Section 3-1 Section 3-1 Example 9: Use the frequency distribution in Example 7 to find the probability of randomly choosing an employee who is not between 25 and 34 years old. SOLUTION: We already know that the probability of an employee being between 25 and 34 years old is 0.366. We also know that the event “not between 25 and 34” is the complement of “between 25 and 34”. So, we can subtract 0.366 from 1 to get the probability that an employee is not between 25 and 34 years old. 1 – 0.366 = 0.634.

  18. Assignments: • Classwork: Pages 142-143 #9 - 24 All • Homework: Pages 143-144 #25 - 36 All

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