# Using Laws of Probability. Sloan Fellows/Management of Technology Summer 2003

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1 Using Laws of Probability Sloan Fellows/Management of Technology Summer 2003

2 Uncertain events Outline The laws of probability Random variables (discrete and continuous) Probability distribution Histogram (pictorial representation) Mean, variance, standard deviation Binomial distribution Summer

3 Modeling Uncertain Events Uncertain (probabilistic) events are modeled as if they were precisely defined experiments with more than one possible outcome.. Each outcome (or elementary event) is clear and well-defined, but we cannot predict with certainty which will occur. Note ambiguity and ignorance refer to not knowing the exact probabilities of outcomes and not knowing all the possible outcomes, respectively. For example, the experiment flipping 3 unbiased coins in order and recording their values (H for heads and T for tails) has as one possible outcome: H 1 T 2 T Summer

4 Sample Space Events The set of all possible outcomes for an experiment is called the Sample Space for the experiment (labeled set S). The set of all possible outcomes for an experiment (Sample Space: S) is mutually exclusive (i.e. outcomes do not overlap) and collectively exhaustive (i.e. comprises all possible outcomes for the experiment) We did not consider a coin landing on its edge Summer

5 Events An event (e.g. event A) is a well-defined collection of outcomes for a particular experiment. We say that event A happens whenever one of the outcomes in event A happens (sounds silly, but rainy days are days when it rains). For example, in our experiment, if we define event A as getting a total of 1 or fewer heads, then, whenever any of the following outcomes happens, we say that event A happened: T 1 T 2 T 3 H 1 T 2 T 3 T 1 H 2 T 3 T 1 T 2 H Summer

6 We can enumerate the sample space for our experiment using a probability tree: H3 H2 0.5 Outcome Probability H1H2H H1 0.5 T3 H1H2T H3 T2 0.5 H1T2H T3 0.5 H1T2T H3 H T3 T1 0.5 T1H2H T1H2T H3 T2 0.5 T1T2H First coin Second coin Third coin 0.5 T3 T1T2T Summer

7 Calculating probabilities Notice that since the coins are unbiased (i.e. p(h)=1/2) all outcomes have the same probability: p(any outcome) = 1/(total # of outcomes)=1/8 We can also calculate the probability for each outcome by multiplying the probabilities on the branches (this would allow us to easily calculate outcome probabilities for biased coins). Notice that, since the outcomes are collectively exhaustive, total probability adds up to 1.00 In general, 0 p(any event) 1.00 (First Law of Probability) Summer

8 Intuitive Probability Which coin toss sequence is most likely? Which is next most likely? (Q#11) 1. H T H T T H 2. H H H H T H 3. H H H T H 23% correct Our intuitions are based on the pattern or look of randomness Actually calculating probability helps us to be explicit about our assumptions and to test them against reality Summer

9 The Gambler s Fallacy We expect that probability is a self-correcting process, such that a run of one type of outcomes will be followed by a run of the opposite to even things out You have started buying stocks on the Internet, beginning with five different stocks. Each stock goes down soon after your purchase. As you prepare to make a sixth purchase, you reason that it should be more successful, since the last five were lemons. lemons. After all, the odds favor making at least one successful pick in six decisions. (Q #12) 93% got this right! Summer

10 The Second Law of Probability If A and B are disjoint (mutually exclusive) events: p(a or B) = p(a B) = p(a) p + p(b) The fool proof (but often brute force) way to calculate the probability of an event is by adding up the probabilities of all the elementary outcomes in the event. Recall event A: one or fewer heads in three tries p(a)= p(t 1 T 2 T 3 )+ p(h 1 T 2 T 3 )+ p(t 1 H 2 T 3 )+ p(t 1 T 2 H 3 ) = 4(1/8) = ½ (since all outcomes are equally likely) Summer

11 The General Second Law We also have (not in the book) that the general form of the second law (to avoid double counting overlapping events) is p(a or B) = p(a B) = p(a) + p(b) - p(a B) Example: Event A (# H <= 1): {H{ 1 T 2 T 3, T 1 H 2 T 3, T 1 T 2 H 3, T 1 T 2 T 3 } Event B (#H>=1): {H{ 1 T 2 T 3, T 1 H 2 T 3, T 1 T 2 H 3, H 1 H 2 H 3, H 1 H 2 T 3, H 1 T 2 H 3, T 1 H 2 H 3 } p(a or B) = p(a B) = p(a) + p(b) - p(a B) = = 4/8 + 7/8 3/8 = 8/8 = 1.0 (as expected!) Summer

12 Notation A B B is A A or B B and the union of A and B A B B is A A and B B and the intersection of A and B I remember them as a boat with oars ( or( or ) ) vs. an ant hill ( and( and hill, sorry) A B A B Summer

13 The Third Law of Probability For events A and B the probability that event A occurred given that B occurred: p(a/b) = p(a B )/p(b); or p(a B ) = p(a/b)p(b) p(a/b) = (3/8)/(7/8)=3/7. (Check: If we know B happened, we know we are focusing on 7 events. Out of these 7 events, 3 belong to event A: 1 head or fewer.) Summer

14 Fourth Law of Probability If A and B are independent events then: p(a/b) = p(a) (1) (knowing knowing that B happened does not change the likelihood of A) Using the 3 rd Law (conditioning on B) and (1): p(a B )= p(a/b)p(b) = p(a) p(b) (2) From the 3 rd Law (but conditioning on A): p(a B )= p(b/a)p(a) (3) Since (3) & (2) are equivalent expressions for p(a B ): Thus, p(a/b)p(b) = p(b/a)p(a) p(a) = p(a) p(b) (4) p(b/a) = p(b) (5) If f A is independent of B; then B is independent of A Summer

15 Probability Tables 100 mutual funds, 40 Rose ose and 60 Fell; from 3 fund companies A, B, C (convert into probabilities by dividing each quantity by 100). Events (example): Probability that a randomly selected fund of these 100 rose and was from company A: p(a R) = 0.3 Firm (A) Firm (B) Firm ( C) Total Fell (F) p(a F) 4 p(b F) 6 p(c F) 30 p(f) 40 Rose (R) p(a R) 30 p(b R) 15 p(c R) 15 p(r) 60 Total p(a) 34 p(b) 21 p(c) Events are disjoint so we can add probabilities across rows and columns: p(a) = p(a F) + p(a R) = 0.34 p(r) = p(a R) + p(b R) + p(c R)= Summer

16 Conditional Probabilities To calculate conditional probabilities we focus on the conditioning event s column/row. Example: The probability that the fund selected fell (F) given that it is from company C: p(f/c) = p(c F)/ p(c) =.30/.45 = 2/3; Also, p(r/c) = p(c R)/ p(c) =.15/.45 = 1/3 Firm (A) Firm (B) Firm (C) Total Fell (F) p(a F) 4 p(b F) 6 p(c F) 30 p(w) 40 Rose (R) p(a R) 30 p(b R) 15 p(c R) 15 p(r) 60 Total p(a) 34 p(b) 21 p(c) Notice these conditional probabilities are for disjoint events so we can add them: p(f/c) + p(r/c) = 2/3 + 1/3 = 1.00 (a fund from company C has to either fall or rise!) Summer

17 Does Statistical Information Make Sense By Itself? A cab was involved in a hit-and and-run accident at night. Two cab companies, the Green and the Blue, operate in the city. 85%of the cabs in the city are Green; 15% are Blue. A witness identified the cab as a Blue cab. When presented with a sample of cabs (half of which were Blue, half Green), the witness was correct 80%. 52% said 80%; 12% said 12% or 15% What is the probability that the cab involved in the accident was Blue? Summer

18 Statistical Information, cont. A cab was involved in a hit-and and-run accident at night. Two cab companies, the Green and the Blue, operate in the city. Although the two companies are roughly equal in size, 85%of the cab accidents in the city involve Green cabs; 15% involve Blue cabs. A witness identified the cab as a Blue cab. When presented with a sample of cabs (half of which were Blue, half Green), the witness was correct 80%. 36% said 80%; 46% said 12% or 15% What is the probability that the cab involved in the accident was Blue? Summer

19 Statistical Information, cont. Medians are 80% in first case; 60% in the second case Cab is: Green Blue Green Witness says: Blue Probability = 12/29 = 41% Summer

20 Bayes Theorem p(b/w b ) = p(w b /B) X p(b) / p(w b ) p(w b ) = p(w b /B) X p(b) + p(w b /G) X p(g) p(b) =.15 p(w b /B) =.80 p(g) =.85 p(w b /G) =.20 p(w b ) =.80 X X.85 =.29 p(b/w b ) =.80 X.15 /.29 = Summer

21 Summary and Next Session The Laws of Probability help us to fill out probability tables and create decision trees Intuitive judgment about probability is often incorrect For Monday s session, you must hand in the Graphics Corp. case by the start of class. Remember, up to two pages of text plus up to six pages of supporting materials Summer

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