Timeline for Gauss-Markov assumptions
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| May 27, 2013 at 1:39 | comment | added | user26091 | Right, sorry for some reason when you said variable before it threw me off as I view Xi as constant in the context of the regression (even though it is a variable by itself) | |
| May 27, 2013 at 1:35 | comment | added | DatamineR | It is actually a design-matrix, and it is rather a multidimensional version of X, the explaining variables. | |
| May 27, 2013 at 1:31 | comment | added | user26091 | Okay that makes sense. One final question though as I still am not sure what the decision matrix is. It is a multi-dimensional version of the Yi? | |
| May 27, 2013 at 1:25 | comment | added | DatamineR | Actually between the variable and errors. But you can only observe the residuals (differences between the observations and the estimated function), while the errors are deviations between the observations and the "true" function, which is unknown. So, you solve the problem using the estimators of errors - the residuals. | |
| May 27, 2013 at 1:10 | vote | accept | user26091 | ||
| May 27, 2013 at 1:09 | comment | added | user26091 | But is it correct to say that what you're talking about is a correlation of zero between Xi and an error term $e_i$ (not a residual, the actual error)? And my apologies, I accepted, I made the same mistake on the last one but went back and accepted. | |
| May 27, 2013 at 0:39 | comment | added | DatamineR | Yes, this is also valid for one-dimensional case. | |
| May 27, 2013 at 0:34 | comment | added | user26091 | Thanks! To clarify, at the moment I am doing the one-dimensional case. Is this to say that the errors and $X_{i}$ values are uncorrelated? | |
| May 27, 2013 at 0:11 | history | answered | DatamineR | CC BY-SA 3.0 |