Probability

1. Probability

2. Random Experiment Say I take a coin and flip it in the air and let it fall on the ground. I might be interested in which side of the coin is face up. The most basic outcomes on the coin are heads or tails. A random experiment is an action or process that leads to one of several possible outcomes. The coin flip is a random experiment. Another random experiment is the time it take to assemble a computer. A sample space of a random experiment is a list of all possible outcomes of the experiment. The outcomes must be (collectively) exhaustive and mutually exclusive.

3. Sample Space and Probability When the sample space is considered probabilities can be assigned to each outcome and the probabilities must follow two rules: 1) The probability of any outcome must be somewhere between 0 and 1 or equal to either 0 or 1, 2) The sum of the probabilities of all the outcomes must equal 1.

4. Mutually Exclusive and Collectively Exhaustive Two outcomes are mutually exclusive if both of the outcomes can not occur simultaneously. A set of outcomesis collectively exhaustive if one of the events must occur. When a coin is flipped heads and tails are mutually exclusive because only one of the values can occur. When a coin is flipped heads and tails are the only outcomes and one must occur so heads and tails are a set of collectively exhaustive events.

5. Types of Probability There are three types of probabilities: classical or a priori probability, empirical probability (the relative frequency method) and subjective probability. A priori probability (where the phrase is italicized and you start out by saying the same thing you say when a doctor puts a popsicle stick in your mouth and then you say pre or e altogether) is a situation where probability is assigned based on prior knowledge of the process involved. Examples of this are that we can assign probability in card games, coin flipping, and die tossing. Other examples will be seen in later chapters. This is sometimes called the classical method of assigning probability.

6. Types of Probability The empirical approach to assigning probability is used when data is available about the past history of the experiment. The probability of an outcome is the relative frequency of the outcome. You can form this probability by taking the ratio of the number of times the outcome came up over the total number of times of the experiment. As an example, if out of 100 sales calls, you had 37 sales, the probability of a sale would be 37/100 = .37. The subjective approach to assigning probability is used when the other two can not be used. You and a friend may consider the experiment that Nebraska will win the national championship in football this year. The outcomes are either they will win or they will not win. You may say P(win) = .4 (meaning the probability of a win is .4) Thus you also say P(not win) = .6. Your friend might say the probabilities should be .7, .3, respectively. Your differences represent your subjectivity.

7. An event Each possible outcome of a random experiment is referred to as a simple event. So, in my tossing a coin a simple event is what happens with the coin – heads or tails is face up. Events in general may be more complex than simple events. An event is a collect or set of one or more simple events in a sample space. The probability of an event is the sum of the probabilities of the simple events that constitute the event.

8. Business Topics So, in my example here I had a coin flip. But this is a class in business. We might talk about did a sale occur or not. Maybe we will be interested in the amount of sales. The universe of business examples is large. We will typically talk about one or two or three variables and each variable has basic outcomes. Remember the basic outcomes are events. But events can be more complex. As an example of a more complex event we might be interested in the event when a die is rolled the result is an even number. Or maybe when we look at store sales on a day (like at each Wal-Mart) we are interested in the outcome of sales being more than $1,000,000.

9. Contingency Table A contingency table is a particular way to view a sample space. Say we talk to 100 customers who just made a purchase in a store and not only do we note the gender of the customer we ask if they paid cash or used a credit card. The responses are summarized on the next screen

10. Contingency Table Payment Method GenderCashCredit CardTotal Female31215 Male 176885 Total 2080100 So, each of the 100 people observed had to be put in a gender category and had to be given a payment method. If you divide each number in the table by the grand total (here 100 and this represents the total number of people observed) the table is then called a joint probability table. Let’s do this and see what results on the next screen.

11. Joint Probability Table Payment Method GenderCashCredit CardTotal Female.03.12.15 Male .17.68.85 Total .20.801

12. The Intersection of Events The intersection of events A and B (A and B are general ideas) is the EVENT that occurs when both A and B occur. The way we write the intersection is to have A and B or it may be written A ∩ B. The way we write the probability of the intersection is to have P(A and B) or it may be written P(A ∩ B). From our example, if you look inside the joint probability table the value .03 = P(Female and Cash)

13. Marginal Probabilities The total column and the total row are called marginal probabilities because the are written in the “margins” of the table. But, note that adding across the Female row gives a total of .15. This is the P(F), or the probability that you would select a female when talking to someone involved in the study. The other marginal probabilities are P(M) = .85 P(Cash) = .2 and P(Credit Card) = .8

14. Venn Diagram A Venn diagram, named in honor of Mr. Venn, is another way to present the sample space for two variables. The rectangle here represents the sample space. On one variable we have event A and that takes up the space represented by circle A. Ignoring circle B, all the rest of the rectangle is Ac(the complement of A). A similar interpretation holds for B. B A This area represents the intersection of A and B.

15. Joint Probability Payment Method GenderCash (B)CC (B’) Total Female(A) P(A and B) P(A and B’) P(A) Male(A’) P(A’ and B) P(A’ and B’) P(A’) Total P(B) P(B’) 1.0 From the example I have labeled the gender A and A’ and the payment method B and B’ and then I have filled the table out in definitional form.

16. Marginal Probability Note in the joint probability table on the previous screen (which is a contingency table that has been modified by dividing all numbers by the grand total!) that 1) In any row the marginal probability is the sum of the joint probabilities in that row, and 2) In any column the marginal probability is the sum of the joint probabilities in that column. (Also note that the sum of the probability of complements equals 1 –> for example P(A) + P(Ac) = 1

17. Union of Events – the General Addition Rule Sometimes we want to ask a question about the probability of A or B, written P(A or B) = P(A ⋃ B). By the general addition rule P(A or B) = P(A) + P(B) – P(A and B). In our example we have P(A or B) = .15 + 0.20 – 0.03 = 0.32. Let’s think about this some more. How many are Female? 3 + 12= 15! How many paid cash? 3 + 17 = 20! But 3 of these were in both A and B. So, when we ask a question about A or B we want to include all that are A or B, but we only want to include them once. If they are both we subtract out the intersection because it was included in both the row and column total.

18. Final Comment When we look at P(A and B) – the intersection of A and B -both A and B have to have occurred to have a non-zero value here. When we look at P(A or B) – the union of A and B - either A or B have to have occurred to have a non-zero value here. But, many times both did occur and if there is an overlap (intersection) of the two we have to subtract out the overlap.

19. Conditional Probability

20. Conditional Probability As we have seen, P(A) refers to the probability that event A will occur. A new idea is that P(A|B) refers to the probability that A will occur but with the understanding that B has already occurred and we know it. So, we say the probability of A given B. The given B part means that it is known that B has occurred. By definition P(A|B) = P(A and B)/P(B). Similarly P(B|A) = P(A and B)/P(A). Note P(A and B) = P(B and A)

21. Now we have by definition P(A|B) = P(A and B)/P(B). In this definition, B has already occurred. The P(B) is the denominator of P(A|B) and is thus the base of the conditional probability. The intersection of A and B is in the numerator. Since B has occurred, the only way A can have occurred is if there is an overlap of A and B. So we have the ratio probability of overlap/probability of known event. Let’s turn to the example from above. The joint probability table is repeated on the next slide.

22. Joint Probability Table Payment Method GenderCashCredit CardTotal Female.03.12.15 Male .17.68.85 Total .20.801 Let’s say you saw someone pay cash for a purchase, but your view was blocked as to the gender of the person. The P(female│cash) = .03/.20 = .15

23. Joint Probability Table Payment Method GenderCashCredit CardTotal Female.03.12.15 Male .17.68.85 Total .20.801 Let’s say you saw someone a female leave the store having made a purchase, but your view was blocked as to the type of payment. The P(cash│female) = .03/.15 = .20

24. Independent Events Events A and B are said to be independent if P(A|B) = P(A) or P(B|A) = P(B). In the example we have been using P(Female) = .15, and P(Female|Cash) = .15. What is going on here? Well, in this example it turns out that the proportion of females in the study is .15 and when you look at those who paid cash the proportion of females is still .15. So, knowing that the person paid cash doesn’t change your view of the likelihood that the person is female. But, in some case (not here) having information about B gives a different view about A. When P(A|B) ≠ P(A) we say events A and B are dependent events. Similarly, when P(B|A) ≠ P(B) events A and B are dependent.

25. Does a coin have a memory? In other words, does a coin remember how many times it has come up heads and will thus come up tails if it came up heads a lot lately? Say A is heads on the third flip, B is heads on the first two flips. Is heads on the third flip influenced by the first two heads. No, coins have no memory! Thus A and B are independent. (Note I am not concerned here about the probability of getting three heads!) Have you ever heard the saying, “Pink sky in the morning, sailors take warning, pink sky at night sailors delight.” I just heard about it recently. Apparently it is a rule of thumb about rain. Pink sky in the morning would serve as a warning for rain that day. If A is rain in the day and B is pink sky in the morning, then it seems that the P(A|B) ≠ P(A) and thus the probability of rain is influenced by morning sky color (color is really just an indicator of conditions).

26. Let’s think about one more example. If you watched a football team all year you could use the empirical approach to find the probability that it will throw a pass on a given play. Say P(pass)=0.4. This means the probability it will pass on a given play is 0.4. But, if there are 5 minutes left in the game and the team is down 14 points the team will want to pass more. SO, P(pass|down 14 with 5 minutes left) = 0.75, for example. This means the probability of a pass depends on the score and time remaining!

27. I have used some examples to give you a feel about when events are independent and when they are dependent. By simple equation manipulation we change the conditional probability definition to the rule called the multiplication lawor rule for the intersection of events: P(A and B) = P(B)P(A⃒B) or P(A and B) = P(A)P(B⃒A) . Note the given part shows up in the other term. Now this rule simplifies if A and B are independent. The conditional probabilities revert to regular probabilities. We would then have P(A and B) = P(B)P(A) = P(A)P(B). Does this hold in our running example? Sure it does!

28. Say, as a new example, we have A and B with P(A)=.5, P(B)=.6 and P(A and B) =.4 Then a. P(A⃒B) = .4/.6 = .667 b. P(B⃒A) = .4/.5 = .8 c. A and B are not independent because we do NOT have P(A⃒B) = P(A), or P(B⃒A) = P(B). Say, as another example, we have A and B with P(A)=.3 and P(B)=.4 and here we will say A and B are mutually exclusive. This means P(A and B) = 0 (in a Venn Diagram A and B have no overlap), then a. P(A⃒B) = 0/.4 = 0 Here A and B are not independent.

29. Y Y1 Y2 Totals X X1 P(X1 andY1) P(X1 andY2) P(X1) X2 P(X2 andY1) P(X2 andY2) P(X2) Totals P(Y1) P(Y2) 1.00 Here I put the joint probability table again in general terms. Question X has mutually exclusive and collectively exhaustive events X1 and X2. For Y we have a similar set-up. Note here each has only two responses, but what we will see below would apply if there are more than 2 responses. Let’s review some of the probability rules we just went through and then we will add one more rule.

30. Inside the joint probability table we find joint probabilities (like P(X1 and Y1) and in the margins we find the marginal probabilities (like P(X1)). Marginal Probability Rule P(X1) = P(X1 and Y1) + P(X1 and Y2) General Addition Rule P(X1 or Y1) = P(X1) + P(Y1) – P(X1 and Y1) Conditional Probability P(X1|Y1) = P(X1 and Y1)/P(Y1) Multiplication Rule P(X1 and Y1) = P(X1|Y1)P(Y1)

31. The new part is to view the marginal probability rule as taking each part and use the multiplication rule. So, Marginal Probability Rule P(X1) = P(X1 and Y1) + P(X1 and Y2) =P(X1|Y1)P(Y1) + P(X1|Y2)P(Y2) Where Y1 and Y2 are mutually exclusive and collectively exhaustive.