Transcription of Chapter 5: JOINT PROBABILITY DISTRIBUTIONS Part 1 ...
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Chapter 5: JOINT PROBABILITY DISTRIBUTIONS Part 1 ...
Chapter 5: JOINT PROBABILITYDISTRIBUTIONSPart 1: Sections to bothdiscreteandcontinuousrandom variables wewill discuss the JOINT DISTRIBUTIONS (for two or s) Marginal DISTRIBUTIONS (computed from a JOINT distribution ) Conditional DISTRIBUTIONS ( (Y=y|X=x)) Independence sXandYThis is a good time to refresh yourmemory on double-integration. Wewill be using this skill in the upcom-ing a discreteprobability distribution (orpmf) for a Xwith the example (x) we re simultaneously interested intwo or more variables in a random re looking for a relationshipbetween the for s Year in college vs. Number of credits taken Number of cigarettes smoked per day vs. Dayof the weekExamples for s Time when bus driver picks you up of caffeine in bus driver s system Dosage of a drug (ml) vs. Blood compoundmeasure (percentage)2In general, ifXandYare two random variables,the PROBABILITY distribution that defines their si-multaneous behavior is called a JOINT here as a table for two discrete randomvariables, which givesP(X=x,Y=y).
Chapter 5: JOINT PROBABILITY DISTRIBUTIONS Part 1: Sections 5.1 & 5.2 For both discreteand continuousrandom variables we will discuss the following... Joint Distributions (for two or more r:v:’s) Marginal Distributions (computed from a joint distribution) Conditional Distributions (e.g. P(Y = yjX= x)) Independence for r:v:’s Xand Y
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