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P(B|A) P(A and B) = P(A)P(B|A) Or P(B|A) = P( a and B) P(A)

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P(B|A) P(A and B) = P(A)P(B|A) Or P(B|A) = P( a and B) P(A) Brent Clayton Homework #0 Summary of class notes 2/3/2011 The lecture notes for this particular day a centered on sections 2.4-2.5 in the Devore book. Basically the idea of conditional probability is presented here. Conditional probability states that given an event (event B), it will only occur if an event (event A) has already occurred. The probabilty statement can be written as follows: P(B|A) This only applies when the events are independent of each other meaning event A has no effect on the probability of event B happening. The other case involes these two events when they are independent. This scenario produces an intersection of the two events (the probability that both events occur). This can be written as follows: P(A and B) = P(A)P(B|A) or P(B|A) = P( A and B) P(A) Example: (Deck of Cards) B = Red Card Pr(A1) = .25 (Probability the card is a diamond) A(1) = Card is a Diamond Pr(A1 | B) = .50 (Probability that the card is a Diamond given it is Red) A(2) = Card is a Jack Pr(A2 | B) = 1/13 (Probability the card is a Jack of Diamond given it is Red) This brings us to the subject of mutually exclusive events. The definition of being mutually exclusive (disjoint) means that it is impossible for two events to occur together. Given two events, A and B, they are mutually exclusive if (A П B) = 0. If these two events are mutually exclusive, they cannot be independent. Question: If A, B are mutually exclusive, then A, B are independent…? a) Sometimes b) Always c) Never Correct answer is c. Example (Mutually Exclusive Events): A recent study was conducted using both male and female subjects ages 20-30 that wanted to find the average salary of men vs women. For the example here, the mutually exclusive events are the subject in the study could not be both female and male at the same time. The subject also cannot also be aged 22 or 28 (random selection, any of the ages would work in the range given) at the same time. Brian Henderson ST 371 Homework 0 Lecture of 03FEB2011 This lecture summary covers parts of conditional probability. We went through several examples of how to determine the different probabilities using a Venn diagram. We covered also the difference between independent and dependent events. Conditional Probability – the probability that one event will occur given that another event has already occurred. Notation: Probability that A will occur given B has occurred already= Pr(A|B). Pr( ) Pr( | ) = Pr( ) 퐴⋀퐵 퐴 퐵 퐵 Events are independent if Pr(A|B) = Pr(A) and are dependent other wise. Example. Given a Normal Deck of Playing Cards. A=Diamond Card B=Red Card C=Jack Example 1) 1 1 Pr( | ) = 4 = = 50% 1 2 2 퐴 퐵 = 13 1 Pr( ) = = = 25% = 푃푟표푏푎푏푖푙푖푡푦52 4 표푓 푡ℎ푒 푐푎푟푑 푏푒푖푛푔 푎 푑푖푎푚표푛푑 푎푓푡푒푟 푘푛표푤푖푛푔 푡ℎ푎푡 푖푡 푖푠 푎 푟푒푑 푐푎푟푑 Pr( | ) Pr( ) . 퐴 푃푟표푏푎푏푖푙푖푡푦 표푓 푡ℎ푒 푐푎푟푑 푏푒푖푛푔 푎 푑푖푎푚표푛푑 푐푎푟푑 Example 2) →Using same퐴 퐵 event≠ designations퐴 푤ℎ푖푐ℎ 푚푒푎푛푠 as example푡ℎ푎푡 1.푒푣푒푛푡푠 퐴 푎푛푑 퐵 푎푟푒 푑푒푝푒푛푑푒푛푡 표푛 푒푎푐ℎ 표푡ℎ푒푟 2 52 1 Pr( | ) = = = 1 13 2 퐶 퐵 푃푟표푏푎푏푖푙푖푡푦4 1표푓 푡ℎ푒 푐푎푟푑 푏푒푖푛푔 푎 퐽푎푐푘 푎푓푡푒푟 푘푛표푤푖푛푔 푡ℎ푎푡 푖푡 푖푠 푎 푟푒푑 푐푎푟푑 Pr( ) = = = 52 13 Pr( | ) = Pr( ) . 퐶 푃푟표푏푎푏푖푙푖푡푦 표푓 푡ℎ푒 푐푎푟푑 푏푒푖푛푔 푎 퐽푎푐푘 If A is independent→ 퐶 퐵of B, then 퐶A’ is푤 alsoℎ푖푐ℎ independent푚푒푎푛푠 푡ℎ푎푡 of 푒푣푒푛푡푠B. 퐵 푎푛푑 퐶 푎푟푒 푖푛푑푒푝푒푛푑푒푛푡 표푛 푒푎푐ℎ 표푡ℎ푒푟 Brian Henderson ST 371 Homework 0 Lecture of 03FEB2011 Event Independence Vs. Mutually exclusive If events A and B are mutually exclusive of each other then the events will never be independent of each other. Pr( ) 0 Pr( | ) = = = 0 Pr( ) 0 . Pr( ) Pr ( ) 퐴⋀퐵 퐴 퐵 퐴 ≠ 푡ℎ푒푟푒푓표푟푒 푡ℎ푒푦 푤푖푙푙 푛푒푣푒푟 푏푒 푖푛푑푒푝푒푛푑푒푛푡 표푓 푒푎푐ℎ 표푡ℎ푒푟 퐵 퐵 If the events are independent of each other then the Venn diagram can be simplified. Pr( ) + Pr( ) = 1 Pr( ) Pr( ) = Pr( ) Pr( ) + Pr( ) = Pr ( ) ′ 퐴 퐴 퐵 ∗ 퐴 퐴 ⋀ 퐵 퐴 ⋀ 퐵 퐴′ ⋀ 퐵 퐵 John Harrison ST-371-002 02/10/2011 Homework-0 Lecture Notes: ST-371-002 (02/03/2011) I. Review from Tues. Feb. 1st. a) Conditional Probability: What is the probability event A will happen, given that event B already happened. Pr ( ) ( | ) = Pr ( ) 퐴 ∩ 퐵 푃푟 퐴 퐵 퐵 II. New Material. a) Definition: Two events, A and B, are Independent if Pr( | ) = Pr ( ), and are dependent otherwise. 퐴 퐵 퐴 i. Example 1: Given a deck of cards, event B=Red Card and event A1=Diamond Card. Are these two events independent? · Pr( ) = 0.25 · Pr( | ) = 0.50 1 ANSWER:퐴 The two events are dependent. 1 퐴 퐵 ii. EXAMPLE 2: In addition to the above statements, event A2=Jack Card. Are events A2 and B independent? · Pr( | ) = 1 · Pr( 2) = 퐴 퐵 13 ANSWER: The1 two events are independent. 퐴2 13 Note: A2 and B’ are also independent. John Harrison ST-371-002 02/10/2011 b) If two events, A and B, are mutually exclusive and A≠0 and B≠0, are A and B independent? ( ) ( | ) = = · ( ) ( ) Pr 퐴∩퐵 0 · Pr( ) 0 푃푟 퐴 퐵 Pr 퐵 Pr 퐵 ANSWER: The two events are always dependent. 퐴 ≠ c) If two events, A and B, are independent then the probability tree can be redrawn as shown below: · Pr( ) = Pr( ) Pr( | ) = Pr( ) Pr ( ) · Also, Pr( ) + Pr( ) = Pr ( ) 퐴 ∩ 퐵 퐵 ∗ 퐴 퐵′ 퐵 ∗ 퐴 d) The Following is an퐴 example∩ 퐵 of transferring퐵 ∩ 퐴 data퐵 from a table to a tree: Tommy Quinn ST 371 HW0 LECTURE 2/3/11 SUMMARY This lecture first dealt with conditional probability. Conditional probability is the probability of an event given that another event already occurred. Ex) Probability of A given B ( ) ) Pr(A|B) = ( Pr 퐴 ∩퐵 Pr 퐵 Next we talked about Independent events. Two events are independent if P(A|B) = P(A) Ex) Probability that card drawn in event A is a Jack given event B was the drawing of a red card. ( ) Pr(A|B) = ( ) = /2 = 1/13 Pr 퐴∩퐵 52 It was statedPr that퐵 if 26A and52 B are mutually exclusive -> (A B) = 0 then A and B are never independent. ∩ Various examples were then given to demonstrate independent events on a tree diagram. The next item in the lecture was the gumdrop example. Every person in the class randomly picked a red or a green gumdrop from a bowl. It was found that out of the 13 females in the class, 5 chose red, 8 chose green. Out of the 48 males in the class, 24 chose red and 24 chose green. The below tree diagram was made to compute the probabilities of each choice. .
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