- #1
musicgold
- 304
- 19
Hi,
I am confused with respect to these two terms. In a book on regression analysis, I read the following statements.
1. For two normally distributed variables, zero covariance / correlation means independence of the two variables.
2. With the normality assumption, the following equation means that [tex] \mu_i [/tex] and [tex] \mu_j [/tex] are NOT ONLY uncorrelated BUT ALSO independently distributed.
[tex] \left \mu_i - N (0, \sigma^2 \right) [/tex]
Not able to get the wiggly line (~) after ui
I am trying to understand if it is possible to have two variables that are
(a) uncorrelated, and not-independent.
(b) uncorrelated and independent
(c) correlated and not-independent
(d) correlated and independent
I would appreciate it if you could explain each type with one example.
Thanks
MG.
I am confused with respect to these two terms. In a book on regression analysis, I read the following statements.
1. For two normally distributed variables, zero covariance / correlation means independence of the two variables.
2. With the normality assumption, the following equation means that [tex] \mu_i [/tex] and [tex] \mu_j [/tex] are NOT ONLY uncorrelated BUT ALSO independently distributed.
[tex] \left \mu_i - N (0, \sigma^2 \right) [/tex]
Not able to get the wiggly line (~) after ui
I am trying to understand if it is possible to have two variables that are
(a) uncorrelated, and not-independent.
(b) uncorrelated and independent
(c) correlated and not-independent
(d) correlated and independent
I would appreciate it if you could explain each type with one example.
Thanks
MG.
Last edited: