Timeline for Linear regression not fitting well
Current License: CC BY-SA 3.0
13 events
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| Jan 28, 2014 at 21:26 | history | edited | crogg01 | CC BY-SA 3.0 |
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| Jan 28, 2014 at 18:08 | history | edited | crogg01 | CC BY-SA 3.0 |
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| Jan 28, 2014 at 17:38 | history | edited | crogg01 | CC BY-SA 3.0 |
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| Jan 28, 2014 at 17:30 | history | edited | crogg01 | CC BY-SA 3.0 |
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| Jan 28, 2014 at 17:20 | history | edited | crogg01 | CC BY-SA 3.0 |
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| Jan 28, 2014 at 17:09 | history | edited | crogg01 | CC BY-SA 3.0 |
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| Jan 28, 2014 at 17:04 | history | edited | crogg01 | CC BY-SA 3.0 |
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| Jan 28, 2014 at 16:24 | comment | added | whuber♦ | I wish I had known that before I acquired a sample from your image, LOL! | |
| Jan 28, 2014 at 16:19 | comment | added | Timothée HENRY | Thank you. And yes, y is a probability. I also posted the raw data in a related question: stats.stackexchange.com/questions/83576/…, although I called x what I called log(x) in the other question... | |
| Jan 28, 2014 at 16:19 | history | edited | crogg01 | CC BY-SA 3.0 |
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| Jan 28, 2014 at 16:18 | comment | added | whuber♦ |
A comment by the OP, "The y values are probabilities of being of a certain class, obtained from averaging classifications done manually by people," suggests that logistic regression would be inappropriate for these data--although it might be a great solution for the raw data (as suggested in your first paragraph), depending on what the "classifications" are and how the "averaging" occurred. When applied to the data shown in the question, glm produces a relatively flat uncurved line that looks remarkably like the line shown in the question.
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| Jan 28, 2014 at 16:09 | history | edited | crogg01 | CC BY-SA 3.0 |
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| Jan 28, 2014 at 16:03 | history | answered | crogg01 | CC BY-SA 3.0 |