Transcription of Chapter 5: JOINT PROBABILITY DISTRIBUTIONS Part 1 ...
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Chapter 5: JOINT PROBABILITY DISTRIBUTIONS Part 1 ...
Chapter 5: JOINT PROBABILITY . DISTRIBUTIONS . part 1: Sections to For both discrete and continuous random variables we will discuss the JOINT DISTRIBUTIONS (for two or more 's). Marginal DISTRIBUTIONS (computed from a JOINT distribution ). Conditional DISTRIBUTIONS ( P (Y = y|X = x)). Independence for 's X and Y. This is a good time to refresh your memory on double-integration. We will be using this skill in the upcom- ing lectures. 1. Recall a discrete PROBABILITY distribution (or pmf ) for a single X with the example be- x 0 1 2. f (x) Sometimes we're simultaneously interested in two or more variables in a random experiment. We're looking for a relationship between the two variables. Examples for discrete 's Year in college vs. Number of credits taken Number of cigarettes smoked per day vs.
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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