Timeline for Power of a Multiple Linear Regression
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 15, 2013 at 11:14 | vote | accept | cryptic_star | ||
| Apr 15, 2013 at 4:35 | answer | added | Jeremy Miles | timeline score: 1 | |
| Apr 14, 2013 at 20:44 | comment | added | Peter Flom | That would indicate possible overfitting of the data. To give an extreme example, if you have N-1 independent variables, you will get a perfect fit even if all the data is random. | |
| Apr 14, 2013 at 18:52 | comment | added | cryptic_star | Our N is greater than 10 times the number of variables for some models, and less than 10 times the number of variables for other models. What does having an insufficient N indicate, even if you do get significance? | |
| Apr 14, 2013 at 18:40 | comment | added | Peter Flom | Are they worried about power or over-fitting? If your result was significant, you had enough power to detect the effect size that you found. But if your N was less than about 10 times the number of variables, that can be problematic. | |
| Apr 14, 2013 at 18:17 | comment | added | cryptic_star | Interesting. Someone else has already done analysis on this data, where we did achieve significant results. However, someone else was questioning whether we had enough samples to validly perform a multiple linear regression. Does the presence of significance indicate that we do have enough samples, or is there another way to determine this? | |
| Apr 14, 2013 at 18:13 | comment | added | Peter Flom | It's questionable whether one should do post-hoc power analysis at all. See e.g this article. This refers to post-hoc as in after the analysis has been done, not just after the sample has been collected. | |
| Apr 14, 2013 at 18:08 | history | asked | cryptic_star | CC BY-SA 3.0 |