Timeline for Fitting a curve best practice
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Dec 9, 2014 at 16:13 | vote | accept | gbh. | ||
| S Dec 9, 2014 at 9:56 | history | suggested | Gilles | CC BY-SA 3.0 |
improved formatting
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| Dec 9, 2014 at 9:43 | review | Suggested edits | |||
| S Dec 9, 2014 at 9:56 | |||||
| Dec 9, 2014 at 1:06 | comment | added | Glen_b | Ben's approach seems very sensible. You might consider treating the experiment-effect as a random effect (random-intercept) in a mixed effects model. | |
| Dec 8, 2014 at 23:05 | comment | added | whuber♦ | Exactly what is this "relationship"? Is it supposed to be of the form $y=ax^2+bx+c$, $x=ay^2+by+c$, $ax^2+bxy+cy^2+dx+ey+f=0$, or perhaps something else? Which data values are subject to random variation? | |
| Dec 8, 2014 at 22:11 | answer | added | Ben Kuhn | timeline score: 1 | |
| Dec 8, 2014 at 22:07 | comment | added | gbh. | The relationship does not change and a random amount is added. I guess the question is if we can do better than a simple lstsq by somehow clustering data for similar experiments. | |
| Dec 8, 2014 at 21:58 | comment | added | Ben Kuhn | In what way do you expect the experiments to alter $y$? Will it add a constant? Add a random amount? Change the relationship between $x$ and $y$? | |
| Dec 8, 2014 at 21:53 | history | asked | gbh. | CC BY-SA 3.0 |