Timeline for Analyzing residual plot vs independent variables plot
Current License: CC BY-SA 3.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 4, 2014 at 12:41 | answer | added | Andre Silva | timeline score: 1 | |
| Jun 21, 2013 at 22:57 | comment | added | Patrick | oops: Y = b0 + b1*X + e; In addition, the homogeneity of variance (homoscedasticity) assumption is in regards to the errors at each value of X. Independence also refers to these errors. Hopefully this makes the assumption diagnostic tools more relevant to you. | |
| Jun 21, 2013 at 21:38 | comment | added | Patrick | This is because the assumptions you've mentioned all refer to the error term in the regression model and not the original variables. The residuals represent the unexplained variability in Y (that is not explained by X), this is the error term (e) in Y = b1 + b0*X + e. When you look at a QQ-plot of residuals (for example), you are comparing this error term against standard normal counterparts. | |
| Jun 21, 2013 at 20:57 | history | asked | user793468 | CC BY-SA 3.0 |