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1 Probability In this chapter, we will study the topic of probability which is used in many different areas including insurance, science, marketing, and.

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Presentation on theme: "1 Probability In this chapter, we will study the topic of probability which is used in many different areas including insurance, science, marketing, and."— Presentation transcript:

1 1 Probability In this chapter, we will study the topic of probability which is used in many different areas including insurance, science, marketing, and government. Definition: A RANDOM EXPERIMENT is a process or activity which produces a number of possible outcomes. The outcomes cannot be predicted with absolute certainty.

2 2 Examples of Experiments Example 1: Flip two coins and observe the possible outcomes of heads and tails. Example 2: Select two marbles without replacement from a bag containing 1 white, 1 red and 2 green marbles. Example 3: Roll two dice and observe the sum of the points on the top faces. All of the above are considered EXPERIMENTS.

3 3 More Terminology The SAMPLE SPACE is a list of all possible outcomes of the experiment. The outcomes must be mutually exclusive and exhaustive. MUTUALLY EXCLUSIVE means they are distinct and non-overlapping. EXHAUSTIVE means including all possibilities.

4 4 The experiment is to select a card from an ordinary deck of playing cards (no jokers). The sample space consists of the 52 cards, 13 of each suit. We have 13 clubs, 13 spades, 13 hearts and 13 diamonds. An example of a simple event is that the selected card is the two of clubs. An example of a compound event is that the selected card is red (there are 26 red cards and so there are 26 simple events comprising the compound event). Example

5 5 Roll two fair 6-sided dice. Describe the sample space of this event.

6 6 Example Solution There are six outcomes on the first die {1,2,3,4,5,6} and those outcomes are represented by six branches of the tree starting from the “tree trunk”. For each of these, there are six outcomes for the second die, represented by the brown branches. Therefore there are _____ outcomes.

7 7 Example Solution (continued) (1,1), (1,2), (1,3), (1,4), (1,5), (1,6) (2,1), (2,2), (2,3), (2,4), (2,5), (2,6) (3,1), (3,2), (3,3), (3,4), (3,5), (3,6) (4,1), (4,2), (4,3), (4,4), (4,5), (4,6) (5,1), (5,2), (5,3), (5,4), (5,5), (5,6) (6,1), (6,2), (6,3), (6,4), (6,5), (6,6) Sample space of all possible outcomes when two 6-sided dice are tossed:

8 8 Probability of an Event The equally likely assumption means that all simple events have the same probability. The probability of an event is the sum of the probabilities of the simple events that constitute the event. Under the equally likely assumption, the probability of a compound event is the number of elements in the event, divided by the number of events of the sample space. P(E) =

9 9 Theoretical Probability Example Find the probability of a sum of 7 when two fair 6-sided dice are rolled.

10 10 Some Properties of Probability The first property states that the probability of any event will always be a number BETWEEN 0 AND 1 (inclusive). If P(E) = 0, we say that event E is an IMPOSSIBLE EVENT. If P(E) = 1, we call event E a CERTAIN EVENT. The second property states that the sum of the probabilities of all simple events of the sample space must equal 1.

11 11 Steps for Finding the Probability of an Event E Step 1. Set up an appropriate sample space S for the experiment. Step 2. Assign acceptable probabilities to the simple events in S Step 3. To obtain the probability of an arbitrary event E, add the probabilities of the simple events in E.

12 12 Example (continued) Example: Two fair coins are tossed. Find the probability of at least one tail appearing. Solution: “At least one tail” is interpreted as “one tail or two tails.” This is a theoretical probability. Step 1: Find the sample space: There are _______ possible outcomes. Step 2: How many outcomes of the event have “at least one tail”? Step 3: Use P(E)= =

13 13 Recall: Number of Events in the Union (Addition Principle) Event A Event B Sample space S n(A  B) = n(A) + n(B) – n(A  B) The number of events in the union of A and B is equal to the number in A plus the number in B minus the number of events that are in both A and B.

14 14 Addition Rule for Probability

15 15 Example of Addition Rule A single card is drawn from a deck of cards. Find the probability that the card is a jack or club. Set of Jacks Set of Clubs Jack and Club (jack of Clubs) P(J or C) = P(J) + P(C) - P(J and C)

16 16 Union of Mutually Exclusive Events A single card is drawn from a deck of cards. Find the probability that the card is a king or a queen. The events King and Queen are mutually exclusive. They cannot occur at the same time. So the probability of King and Queen is _________. Queens Kings

17 17 Mutually Exclusive Events If A and B are mutually exclusive then The intersection of A and B is the empty set. A B

18 18 Example Two 6-sided dice are tossed. What is the probability of a sum greater than 8, or doubles? P(S>8 or doubles) = P(S>8) + P(doubles) - P(S>8 and doubles)

19 19 Complement Rule Many times it is easier to compute the probability that A won’t occur, than the probability of event A: Example: What is the probability that when two dice are tossed, the number of points on each die will not be the same? This is the same as saying that doubles will not occur. Since the probability of doubles occurring is 6/36 = 1/6, then the probability that doubles will not occur is:

20 20 Conditional Probability, Intersection and Independence Consider the following problem: Find the probability that a randomly chosen person in the U.S. has lung cancer. We want P(C). To determine the answer, we must know how many individuals are in the sample space, n(S). Of those, detrmine how many have lung cancer, n(C) and find the ratio of n(C) to n(S).

21 21 Conditional Probability Now, we will modify the problem: Find the probability that a person has lung cancer, given that the person smokes. Do we expect the probability of cancer to be the same? What we now have is called conditional probability. It is symbolized by and means the probability of lung cancer assuming or given that the person smokes.

22 22 Example Two cards are drawn without replacement from an ordinary deck of cards. Find the probability that two clubs are drawn in succession. Since we assume that the first card drawn is a club, there are 12 remaining clubs and 51 total remaining cards. =

23 23 Example A coin is tossed and a die is rolled. Find the probability that the coin comes up heads and the die comes up three.

24 24 The probability of getting a three on the die does not depend upon the outcome of the coin toss. We say that these two events are INDEPENDENT, since the outcome of either one of them does not affect the outcome of the remaining event. Example (continued)

25 25 Examples of Independence 1. Two cards are drawn in succession with replacement from a standard deck of cards. What is the probability that two kings are drawn? 2. Two marbles are drawn with replacement from a bag containing 7 blue and 3 red marbles. What is the probability of getting a blue on the first draw and a red on the second draw?

26 26 Dependent Events Two events are dependent when the outcome of one event affects the outcome of the second event. Example: Draw two cards in succession without replacement from a standard deck. Find the probability of a king on the first draw and a king on the second draw.

27 27 Other Examples A fair die is rolled 5 times. A)What is the probability of getting a 6 on the 5 th roll, given that a 6 turned up on the preceding 4 rolls? B)What is the probability that a “2” turns up every time?

28 28 Other Examples A fair coin is tossed 4 times. A)What is the probability of tossing a tail on the 4 th toss, given that the preceding 3 tosses were tails? B)What is the probability of getting 4 heads or 4 tails?


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