Timeline for Independence of data points assumption
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jun 24, 2015 at 12:25 | comment | added | Udaya | I agree with you. That was what my point was, actually. | |
| Jun 24, 2015 at 12:20 | comment | added | brumar | Yes, I like the way your book put it. But that does not discard my answer though. If you don't model your "random process" properly (for example by not considering in the model obvious design-related effects like the one you pointed out), this is wrong and we are used to call this wrongness i.i.d violation. | |
| Jun 24, 2015 at 11:57 | comment | added | Udaya | This iid assumption is an artifact of modeling a physical phenomenon as a random process. The moment we assume that a physical phenomenon is a random process, we should also be able to assume that it's observations are, in fact, iid. | |
| Jun 24, 2015 at 11:55 | comment | added | Udaya | Actually, I got an answer to this question. In the book, iid is used in context of probabilistic distributions. As an example, if we take a coin toss experiment, we would naturally assume that subsequent trials are independent of previous ones. Likewise, any probabilistic distribution applies the same way (Even continuous ones like a Gaussian). Coin toss is an example of a multinomial distribution, in this case. | |
| Jun 24, 2015 at 10:56 | history | answered | brumar | CC BY-SA 3.0 |