{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

Stat400Lec13(Ch4.1)

# Stat400Lec13(Ch4.1) - STAT 400 Chapter 4.1 Spring 2012...

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

STAT 400 Chapter 4.1 Spring 2012 Chapter 2- 3: Univariate distirubitons (Review) Discrete r.v. o Uniform discrete, hypergeometric, bernoulli, binomial, poisson , negative binomial Continuous r.v. o Uniform, cauchy, exponential, gamma, chi-square, normal (gaussian) Important Statistics: o Mean, variance, standard deviation, moment generating function, median Distribution: o p.m.f. c.d.f (discrete) o p.d.f c.d.f (continuous) Chapter 4: Bivariate distributions Let X and Y be two discrete random variables. The joint probability mass function p ( x , y ) is defined for each pair of numbers ( x , y ) by p ( x , y ) = P( X = x and Y = y ). Let A be any set consisting of pairs of ( x , y ) values. Then P ( ( X, Y ) A ) =       y x A y x p , , . Let X and Y be two continuous random variables. Then f ( x , y ) is the joint probability density function for X and Y if for any two-dimensional set A P ( ( X, Y ) A ) =    A dy dx y x f , . Joint p.m.f. for two discrete r.v.s. joint p.d.f for two continuous r.v.s.

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

View Full Document
1. Consider the following joint probability distribution p ( x , y ) of two discrete random variables X and Y: x \ y 0 1 2 1 0.15 0.10 0 2 0.25 0.30 0.20 Is this a valid probability model? a)
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### Page1 / 8

Stat400Lec13(Ch4.1) - STAT 400 Chapter 4.1 Spring 2012...

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

View Full Document
Ask a homework question - tutors are online