Timeline for Parallel straight lines on residual vs fitted plot
Current License: CC BY-SA 3.0
10 events
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| Jun 28, 2017 at 8:12 | history | edited | ttnphns | CC BY-SA 3.0 |
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| Oct 3, 2012 at 10:59 | vote | accept | Datageek | ||
| Sep 28, 2012 at 14:40 | history | edited | Datageek | CC BY-SA 3.0 |
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| Sep 27, 2012 at 19:00 | answer | added | Emmanuel Charpentier | timeline score: 5 | |
| Sep 27, 2012 at 16:09 | comment | added | whuber♦ | You're right about the explanation; your reference nailed it. But your situation looks unusual: it appears you have only ten or so independent responses (which lie on a continuous scale, not a discrete one) but you are using multiple explanatory variables that vary over time. This is not a situation contemplated by most regression techniques. More information about what these variables mean and how they are measured might help us identify a good analytical approach. | |
| Sep 27, 2012 at 15:51 | comment | added | Datageek | Actually the Y is the price we try to predict, which changes every few months. We have weekly-recorder variables (X) for the corresponding price (Y) that changes every few months. Would logistic regression work in this case when we don't know future price? | |
| Sep 27, 2012 at 15:50 | history | edited | Datageek | CC BY-SA 3.0 |
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| Sep 27, 2012 at 15:20 | comment | added | Peter Flom |
You almost certainly do need some other form of regression. If the Y data are ordinal (which I suspect) then you probably want ordinal logistic regression. One R package that does this is ordinal, but there are others as well
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| Sep 27, 2012 at 15:12 | history | edited | Datageek | CC BY-SA 3.0 |
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| Sep 27, 2012 at 15:07 | history | asked | Datageek | CC BY-SA 3.0 |