Section3.1-3.2-students_SP11 - Recap. Conditional...

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1 Section 2.4, 2.5 1 Recap…. Independence: Bayes’ Theorem: Conditional Probability: Law of Total Probability: Sections 2.4-2.5 Today…. • Discrete Random Variables : • Probability Mass Functions : • Cumulative Distribution Functions : X : S R X p X (x) = P( X = x) = P ( { s ε : X(s) = x } ) F X (x) = P( X x) = P ( { s ε : X(s) x } ) R real line [ ] 0 1 X(s) random variable p X (x) pmf F X (x) cdf P({s}) probability function Next class ….
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Random Variables (RV) In most applications we are usually interested in one or more real valued properties of the outcome of the experiment: 1) lifetime of an electronic part 2) lifetime of a prosthetic device 3) # of defects in a semiconductor wafer 4) yield of a crop (lbs/acre) Sections 3.1-3.2 R X = range (X) RV: Examples 1. Flip a fair coin two times. There is no one unique random variable X . The random variable you work with is often related to a research question – e.g., “is the coin really fair”? Sections 3.1-3.2
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Section3.1-3.2-students_SP11 - Recap. Conditional...

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