notes509fall11sec34

notes509fall11sec34 - STAT 509 Section 3.4: Continuous...

This preview shows pages 1–3. Sign up to view the full content.

STAT 509 – Section 3.4: Continuous Distributions Probability distributions are used a bit differently for continuous r.v.’s than for discrete r.v.’s. A continuous random variable is one for which the outcome can be any value in an interval of the real number line. Examples Let Y = length in mm Let Y = time in seconds Let Y = temperature in ºC Continuous distributions typically are represented by a probability density function (pdf), denoted f ( y ). Properties of Density Functions: (1) f ( y ) ≥ 0 for all possible y values. (Density function always on or above the horizontal axis) (2) dy y f ) ( = 1. (Total area beneath the curve is exactly 1.) (3) The cumulative distribution function (cdf) is again denoted by F( y ). F( y ) = P(Y < y ) = y dt t f ) (

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
(4) For continuous r.v.’s, the probability distribution will give us the probability that a value falls in an interval (for example, between two numbers). That is, the probability distribution of a continuous r.v. Y will tell us P( y 1 Y y 2 ), where y 1 and y 2 are particular numbers of interest. Specifically, P( y 1 Y y 2 ) is the area under the density function between y = y 1 and y = y 2 : P( y 1 Y y 2 ) = 2 1 ) ( y y dy y f = F( y 2 ) – F( y 1 ) Example Picture:
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 12/13/2011 for the course STAT 509 taught by Professor Chalmers during the Fall '08 term at South Carolina.

Page1 / 9

notes509fall11sec34 - STAT 509 Section 3.4: Continuous...

This preview shows document pages 1 - 3. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online