Chapters 14 15 part 2 probability trees odds
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Chapters 14, 15 (part 2) Probability Trees, Odds. Probability Trees: A Graphical Method for Complicated Probability Problems. Odds and Probabilities. Probability Tree Example: probability of playing professional baseball.

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Chapters 14, 15 (part 2) Probability Trees, Odds

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Chapters 14 15 part 2 probability trees odds

Chapters 14, 15 (part 2) Probability Trees, Odds

Probability Trees: A Graphical Method for Complicated Probability Problems.

Odds and Probabilities


Probability tree example probability of playing professional baseball

Probability Tree Example: probability of playing professional baseball

  • 6.1% of high school baseball players play college baseball. Of these, 9.4% will play professionally.

  • Unlike football and basketball, high school players can also go directly to professional baseball without playing in college…

  • studies have shown that given that a high school player does not compete in college, the probability he plays professionally is .002.

    Question 1: What is the probability that a high school baseball player ultimately plays professional baseball?

    Question 2: Given that a high school baseball player played professionally, what is the probability he played in college?


Chapters 14 15 part 2 probability trees odds

Question 1: What is the probability that a high school baseball player ultimately plays professional baseball

.061*.094=.005734

.939*.002=.001878

P(hs bb player plays professionally)

= .061*.094 + .939*.002

= .005734 + .001878

= .007612


Chapters 14 15 part 2 probability trees odds

Question 2: Given that a high school baseball player played professionally, what is the probability he played in college?

.061*.094=.005734

.061*.094=.005734

P(hs bb player plays professionally)

= .005734 + .001878

= .007612

.939*.002=.001878


Example aids testing

Example: AIDS Testing

  • V={person has HIV}; CDC: Pr(V)=.006

  • P : test outcome is positive (test indicates HIV present)

  • N : test outcome is negative

  • clinical reliabilities for a new HIV test:

    • If a person has the virus, the test result will be positive with probability .999

    • If a person does not have the virus, the test result will be negative with probability .990


Question 1

Question 1

  • What is the probability that a randomly selected person will test positive?


Probability tree approach

Probability Tree Approach

  • A probability tree is a useful way to visualize this problem and to find the desired probability.


Probability tree

Probability Tree

Multiply

branch probs

clinical reliability

clinical reliability


Question 1 what is the probability that a randomly selected person will test positive

Question 1: What is the probability that a randomly selected person will test positive?


Question 2

Question 2

  • If your test comes back positive, what is the probability that you have HIV?

    (Remember: we know that if a person has the virus, the test result will be positive with probability .999; if a person does not have the virus, the test result will be negative with probability .990).

  • Looks very reliable


Question 2 if your test comes back positive what is the probability that you have hiv

Question 2: If your test comes back positive, what is the probability that you have HIV?


Summary

Summary

  • Question 1:

  • Pr(P ) = .00599 + .00994 = .01593

  • Question 2: two sequences of branches lead to positive test; only 1 sequence represented people who have HIV.

    Pr(person has HIV given that test is positive) =.00599/(.00599+.00994) = .376


Recap

Recap

  • We have a test with very high clinical reliabilities:

    • If a person has the virus, the test result will be positive with probability .999

    • If a person does not have the virus, the test result will be negative with probability .990

  • But we have extremely poor performance when the test is positive:

    Pr(person has HIV given that test is positive) =.376

  • In other words, 62.4% of the positives are false positives! Why?

  • When the characteristic the test is looking for is rare, most positives will be false.


Odds and probabilities

ODDS AND PROBABILITIES

World Series Odds

From probability to odds

From odds to probability


From probability to odds

If event A has probability P(A), then the odds in favor of A are P(A) to 1-P(A). It follows that the odds against A are 1-P(A) to P(A)

If the probability the Boston Red Sox win the World Series is .20, then the odds in favor of Boston winning the World Series are .20 to .80 or 1 to 4. The odds against Boston winning are .80 to .20 or 4 to 1

From Probability to Odds


From odds to probability

If the odds in favor of an event E are a to b, then

P(E)=a/(a+b)

If the odds against an event E are c to d, then

P(E’)=c/(c+d)

(E’ denotes the complement of E)

From Odds to Probability

E = win World Series


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