Joint_Distributions

  1. What is this called?
    • Random vector
    • (there are three random variables in this random vector)
  2. Given the joint distribution, you can find any marginal information that you want
    T/F
    True
  3. Given marginal information, you cannot calculate the joint unless...?
    They are independent
  4. What are the two most commonly used measures of association between random variables?
    • Covariance
    • Correlation
  5. What is the covariance computing formula?
  6. Why is covariance difficult to interpret compared to correlation?
    • Covariance has units, so interpretation is difficult
    • Correlation is unitless
  7. What is indicated when ?
    then X and Y are uncorrelated (Not Independent!!!)
  8. What is the value of ρ when X and Y are independent?


    (Alternately, If given rho=0, it translates to X and Y being uncorrelated, not independent!)
  9. What is the range of correlation?

  10. What does this indicate?
    an exact linear relationship
  11. When it reasonable to use a binomial distribution when a hyper-geometric distribution would normally otherwise be considered?
    When sample size is small relative to our population size
  12. point mass
    A probability distribution with unity probability mass at one specific outcome, and zero probability mass everywhere else
  13. How is 'e' defined?
  14. A binomial distribution can be represented by a ___?____ when n is large
    Poisson
  15. Taylor series expansion:
Author
saucyocelot
ID
363326
Card Set
Joint_Distributions
Description
Joint distributions
Updated