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15 Chances, Probabilities, and Odds

15 Chances, Probabilities, and Odds. 15.1 Random Experiments and Sample Spaces 15.2 Counting Outcomes in Sample Spaces 15.3 Permutations and Combinations 15.4 Probability Spaces 15.5 Equiprobable Spaces 15.6 Odds. Random Experiment.

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15 Chances, Probabilities, and Odds

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  1. 15 Chances, Probabilities, and Odds 15.1 Random Experiments and Sample Spaces 15.2 Counting Outcomes in Sample Spaces 15.3 Permutations and Combinations 15.4 Probability Spaces 15.5 Equiprobable Spaces 15.6 Odds

  2. Random Experiment In broad terms, probability is the quantification of uncertainty. • random experiment : an activity or a processwhose outcome cannot be predicted ahead of time. • Examples: tossing a coin, rolling a pair of dice, drawing cards out of a deckof cards, predicting the result of a football game, and forecasting the path of ahurricane.

  3. Sample Space sample space: the set of all possible outcomes of the experiment. For the sake of simplicity, wewill concentrate on experiments for which there is only a finite set of outcomes.

  4. Sample Space - Set Notation Since the sample space of any experiment is a set of outcomes, we will useset notation to describe it. The letter S will be used to denote a samplespace The letter N will be used to denote the size of the sample space S

  5. Examples • Toss a coin once. What is S? What Is N? • Toss a coin twice. Now what is S, N? • Toss two distinguishable coins (say, a nickel and a quarter)at the same time. • Toss a coin twice and observe the number of heads. Now what is S, N? • Same as above but now observe the percentage of tosses that are heads?

  6. More Examples Roll a pair of dice and consider thetotal of the two numbers rolled. Roll a pair of dice and consider the pair rolled. (see next slide)

  7. The dice are colored white andred, a symbolic way to emphasize the fact that we are treating the dice as distinguishable objects. Groups pg. 577 # 2

  8. More Coins ? What if we toss a coin 3 times? 4? 12? In this case the sample space S is toobig to write down, but we can “count” the number of outcomes in S without having to tally them one by one. How? Do you see the pattern?

  9. Multiplication Rule The rule we just used is called themultiplication rule. Informally, the multiplication rule simply saysthat when something is done in stages, the number of ways it can be done isfound by multiplying the number of ways each of the stages can be done. Pg. 577 # 10, 12a Groups # 12b, 14

  10. What is Probability? If we toss a coin in the air, what is the probability that it will land heads up? Most agree that the standard answer given is 1 outof 2, or 1/2. But why is the answer 1/2 and what does such an answer mean? The probability of Heads is 1/2 not because Heads is one out of two possible outcomes but because, if we were to toss the coin over and over in the long run about half of the tosses will turn out to beheads.

  11. Events An event is any subset of the sample space. That is, an event is any set of individual outcomes. This definition includes the possibility of an “event” that has nooutcomes as well as events consisting of a single outcome.

  12. Events Sometimes an event consists of just one outcome. We will call such an event asimple event. In some sense simple events are the building blocks for all otherevents There is also the special case of the empty set { },corresponding to an event with no outcomes. Such an event can never happen,and thus we call it the impossible event.

  13. Probability Assignment Aprobability assignment is a function that assigns to each event E a number between 0 and 1, which represents the probability of the event E and which we denote byPr(E). A probability assignment always assigns probability 0 to the impossibleeventand probability 1 to the whole sample space

  14. Probability Assignment A probability assignment is defined by assigningprobabilities to just the simple events in the sample space. Find the probability of any event by simply adding the probabilities of the individual outcomes that make up that event. There are only two requirements for avalid probability assignment: (1) All probabilities are numbers between 0 and 1,and (2) the sum of the probabilities of the simple events equals 1.

  15. Probability Space Once a specific probability assignment is made on a sample space, the combination of the sample space and the probability assignment is called a probabilityspace. The following is a summary of the key facts related to probability spaces.

  16. ELEMENTS OF A PROBABILITY SPACE ■Sample space:S = {o1, o2,…,oN} ■Probability assignmentPr(o1),Pr(o2), …, Pr(oN) ■Events: These are all the subsets of S, including { } and S itself. The probability of an event is given by the sum of the probabilities of the individual outcomes that make up the event. Pg 580 # 44a,c, 50a,c Groups 44b,d, 50b,d

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