University:
Amherst College
Course:
STAT 360 | Probability
Academic year:

2020
Views:

107

Pages:

27
Author:

Glenn Gomez

Probability September 17th – 22nd ________________________________________ 1) Place the importance of probabilistic thinking within the practice of statistics. 2) Define key terms, discuss important facts, and demonstrate the notation associated with probability. 3) Define compound events and the rules used to determine probability. • Union: Additive Rule • Intersection: Multiplicative rule 4) Define mutual exclusivity and independence. 5) Describe interesting applications to real-world questions: • Total Probability formula • Bayes Theorem • Simpson's Paradox • Discrete Polling • Winning NCAA tournament pools Subjective vs. Objective Probability ________________________________________ Subjective Probability What is the probability that I will get a ticket if I don’t put money in the parking meter? The weather forecast calls for a 70% chance of showers. ________________________________________ Objective Probability If N different events are possible, and each event is equally likely, then the probability of any one of those events occurring is 1/N, and the probability of X of those events occurring is X/N. The tricks: a) b) Where does probability come from? ________________________________________ Gambling a) Discrete number of outcomes b) Trials can be repeated over and over under largely the same circumstances ________________________________________ Why is it important? 1) 2) 3) Key Definitions ________________________________________ EXPERIMENT: SAMPLE POINT: SAMPLE SPACE: EVENT: PROBABILITY (chance): the percentage of time that a particular outcome is expected to occur when the basic experiment is done over and over again Important Probability Facts ________________________________________ 1) The probability of a given simple point (or a given event) may or may not be known with certainty. 2) The probability of a given sample point must be 3) The probability of all sample points must = 1. 4) The probability of an event plus its complement ________________________________________ Notation: Fair Example ________________________________________ You and Biff go to the Big E. Biff is seduced by a carnie who shouts, "Hey young man, bet you can't knock over three stacked milk cans with one softball. If you can, I'll give you this impossibly small stuffed animal!" Biff can't resist the challenge but, in typical fashion, needs to borrow $1 to play. 1) What are the sample points for this experiment? 2) What is the probability that Biff knocks down all three cans? 3) Assuming that the answer to question 2 is 60%, what is the probability that Biff fails to win the stuffed animal? Compound Events ________________________________________ A compound event is an event composed of Two types: 1) Union: AB 2) Intersection: AB Additive Rule of Probability ________________________________________ P(A  B) = P(A) + P(B) – P(A  B) Unless A and B are mutually exclusive: If A and B are mutually exclusive, then the additive rule simplifies to the following: ________________________________________ Note: Mutually exclusive  complementary. Applying the Additive Rule ________________________________________ Event A: You are a woman. Event B: You have blond hair. What is P(A  B)? ________________________________________ Event A: You are a woman Event B: You are a man What is P(A  B)? ________________________________________ Event A: You are wearing blue Jeans Event B: You are under 5’8” Event C: You love Statistics!! What is P(A  B)? What is P(A  C)? What is P(A  B  C)? The Multiplicative Rule of Probability ________________________________________ Probability that both A and B will occur simultaneously is equal to the probability of A times the probability of B given that A occurred. P ( A  B) = P ( A)  P( B | A) where P( A  B) P(A B ) = P( B) Applying the Multiplicative Rule ________________________________________ Event A: You are a woman. Event B: You have blond hair. What is P(A  B)? ________________________________________ Event A: You are a woman Event B: You are a man What is P(A  B)? ________________________________________ Event A: You are wearing blue Jeans Event B: You are under 5’8” Event C: You love Statistics!! What is P(A  B)? What is P(A  C)? What is P(A  B  C)? Independence ________________________________________ Two events are said to be independent if or In other words, knowing that A (or B) occurred does not help you predict whether B (or A) also occurred. VERY IMPORTANT NOTE: Simple example of Independence ________________________________________ A: Draw a spade B: Draw a king P(A) P(B) = = What is P(A|B)? In other words, what is the probability that the card in my hand is a spade if I tell you that it is a king? What is P(B|A)? In other words, what is the probability that the card in my hand is a king if I tell you that it is a spade? Why do we care about independence? ________________________________________ In the long-term, independence will be very important for a number of reasons that are too complicated to discuss right now. In the short-term, independence can help simplify the multiplicative rule formula. If two events are independent then: P (AB) = ________________________________________ If I draw a card from a deck, what is the probability that it is the king of spades? P(A) P(B) = = 1/4 1 / 13 Because A and B are independent: P(AB) = (1/4) • (1/13) = 1 / 52 More Independence ________________________________________ Experiment #1: I draw two cards from a deck. A B = = First Card is a heart Second Card is a heart Are A and B independent? ________________________________________ Experiment #2: I draw one card from a deck, put it back and then draw a second card. A B = = First Card is a heart Second Card is a heart Are A and B independent? ________________________________________ Proving Independence – Joint Probability Table ________________________________________ Youngst Middle Oldest Females .20 .20 .25 .65 Males .15 .05 .15 .35 .35 .25 .40 1.00 Are "Female" and "Youngest" independent? Method 1: Is P(A|B) = P(A)? Is P(B|A) = P(B)? Method 2: Is P(AB) = P(A) P(B)? Bar Brawlin’ Biff ________________________________________ Bar Brawl Biff Shows Up Biff Stays Home No Brawl .06 .56 .80 .70 1.00 What is P(Biff shows up)? What is P(No brawl)? What is P(Biff Stays home  No brawl)? What is P(Biff shows up | No brawl)? What is P(Biff shows up  No brawl)? Are Biff Shows Up and Bar Brawl independent? Tree Diagrams ________________________________________ Golden rule: # of outcomes = PT P= T= Tree Diagrams with Replacement: 1) Assuming that one is equally likely to have a boy as a girl, what is the probability that a family of three children has exactly one girl and two boys? 2) What if the family in question is drawn from an isolated group of people on some Pacific Island where the probability of having a girl is twice the probability of having a boy? Tree Diagrams without replacement: 1) You have a drawer with two blue socks, and two green socks. If you randomly draw two socks without replacement (and without looking), what is the probability that the two socks will form a matching pair? Total Probability ________________________________________ P(A) = P(A|T1) P(T1) + P(A|T2) P(T2) + P(A|T3)P(T3) +…+ P(A|Tn) P(Tn) Iff: P(T1) + P(T2) + P(T3) + P(Tn) = 1 ________________________________________ Probability that I will be in a good mood is .95 if Duke beats Carolina and .20 if Duke loses. The probability of Duke winning is .65 and the probability of them losing is .35. What is the total probability that I will be in a good mood? P(Good Mood) = P(GM|Duke)P(Duke) + P(GM|UNC)P(UNC) Total Probability: San Diego Example ________________________________________ There is a 85% chance that your Spring Break flight from Bradley to San Diego will take off if the skies are clear, a 75% chance if it is raining and a 60% chance if it is snowing, what is the probability that your flight will leave if there is a 50% chance of clear skies, a 30% chance of rain, and a 20% chance of snow? Bayes Theorem ________________________________________ P( A| B) = P( B| A)  P( A) [ P( B| A)  P( A)] + [ P( B| A`)  P( A`)] ________________________________________ What is it good for? Testing situations like random drug tests where we know what the decision of our test instrument is, but we don't know the true state of affairs. Developing a drug test: the METAL test ________________________________________ I am developing a new drug test called Methyltransaminolipide (METAL). I collect urine samples and analyze the amount of METAL in each one. I believe that a METAL of greater than 12 mg / ml is indicative of marijuana use. Using Bayes Theorem to evaluate the METAL test ________________________________________ What is the probability that a person is a marijuana user given that they score over 12 (positive result) on the METAL test given the following conditions? P(+|MJ) = .95 P(+|CG) = .10 P(MJ) = .01 ________________________________________ Bayes Theorem P(MJ|+) = P(+|MJ) P(MJ) . P(+|MJ) P(MJ) + P(+|CG) P(CG) P(MJ|+) =  .09 Using Bayes Theorem II: Changing the base rate ________________________________________ What is the probability that a person is a marijuana user under the same conditions as above except the base rate of marijuana use is higher? P(+|MJ) = .95 P(+|CG) = .10 P(MJ) = .30 ________________________________________ Bayes Theorem P(MJ|+) = P(MJ|Test+) = Return to Conditional Probability ________________________________________ If P(A|B) = .50 Does P(A`|B) = .50?? What about P(A|B`)?? P(A|B) + (A`|B) = 1.00, whereas you cannot determine P(A|B`) if you are only told P(A|B)! ________________________________________ A = Win the race P(A|B) = B = Run a 4:00 mile .50 That means the probability of winning is .50 IF you can run a 4:00 mile. It also means that the probability of you not winning the race IF you run a 4:00 mile is .50 - P(A`|B). ________________________________________ Simpson's Paradox ________________________________________ Application success in various graduate programs at Hypothetical University, broken down by gender Department Males Females Psychology 1 / 5 (.20) 6 / 16 (.375) Biology 10 / 20 (.50) 3 / 5 (.60) Chemistry 15 / 25 (.60) 6 / 9 (.67) Physics 1 / 10 (.10) 4 / 20 (.20) Total Discrete Polling ________________________________________ How can I use probability to elicit sensitive information from you while protecting your privacy? ________________________________________ Raise your hand if… a) the last digit of your SS# is even or b) you were born in Massachusetts. ________________________________________ Raise your hand if… a) the last digit of your SS# is even or b) you dread this class and are only taking it because it is required.

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