Transcription of Chapter 5: JOINT PROBABILITY DISTRIBUTIONS …
{{id}} {{{paragraph}}}
Example: barber
Chapter 5: JOINT PROBABILITY DISTRIBUTIONS …
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. Day of the week Examples for continuous 's Time when bus driver picks you up vs. Quantity of caffeine in bus driver's system Dosage of a drug (ml) vs.
If Xand Yare discrete, this distribution can be described with a joint probability mass function. If Xand Yare continuous, this distribution can
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document:
Related search queries
Joint probability distributions, Probability, Unified Syllabus of Statistics Course Instruction, Probability Distributions, Plotting of observations on probability paper, Econometrics II Lecture 2: Discrete Choice Models, Statistical Decision Theory: Concepts, Methods, Continuous, Probability and mathematical statistics