Timeline for Distribution function of dependent variable, confidence regions
Current License: CC BY-SA 2.5
10 events
| when toggle format | what | by | license | comment | |
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| Jan 26, 2010 at 17:14 | comment | added | user3561 | This question can be answered, but you need to be more specific: a) what is the space on which x lies ? (i.e. is x a vector or a matrix) b) how many observations you have ? (what is the dimension of y) | |
| Jan 22, 2010 at 11:16 | vote | accept | Alex | ||
| Jan 22, 2010 at 6:36 | history | edited | Pete L. Clark | CC BY-SA 2.5 |
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| Jan 22, 2010 at 1:47 | answer | added | pinus | timeline score: 1 | |
| Jan 21, 2010 at 17:04 | comment | added | Douglas Zare | That could happen from one very low probability outlier which is far above the mean for both random variables, so you still couldn't say much of anything. You are pushing on a rope. You need some other tools than the linear regression or correlation to say anything meaningful about either the density function or confidence regions. | |
| Jan 21, 2010 at 15:33 | comment | added | Alex | what would change if correlation were 0.95? | |
| Jan 21, 2010 at 14:45 | comment | added | Douglas Zare | Regression doesn't mean equality. Indeed, the linear fit can be extremely bad. So, you can't say much at all about one variable given a linear regression with a known variable when the correlation is arbitrary. | |
| Jan 21, 2010 at 12:30 | history | edited | Alex | CC BY-SA 2.5 |
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| Jan 21, 2010 at 12:20 | history | edited | Alex | CC BY-SA 2.5 |
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| Jan 21, 2010 at 11:10 | history | asked | Alex | CC BY-SA 2.5 |