Timeline for Model selection with LR test: how to interpret the result?
Current License: CC BY-SA 4.0
10 events
| when toggle format | what | by | license | comment | |
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| S Dec 9, 2019 at 22:34 | history | suggested | Steffen Moritz | CC BY-SA 4.0 |
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| Dec 9, 2019 at 20:56 | review | Suggested edits | |||
| S Dec 9, 2019 at 22:34 | |||||
| Jan 5, 2017 at 13:08 | answer | added | IWS | timeline score: 2 | |
| Jan 5, 2017 at 12:46 | history | edited | Richard Hardy | CC BY-SA 3.0 |
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| Jan 5, 2017 at 6:57 | comment | added | Michael R. Chernick | The question may also be off topic because it may be primarily a programming issue. I am not familiar with Irteat and that is why I hesitate to answer. The short answer is that in statistics we form a null and alternative hypothesis. The way Jerzy Neyman set this up is to try to find evidence to reject the null hypothesis. A high p-value means you can't reject and a low one means you can reject. We don't view this in terms of which hypothesis is better. Let's just say that if you reject the null hypothesis you favor the alternative. So it is just a matter of identifying the null. | |
| Jan 5, 2017 at 6:38 | comment | added | lsadelaide | You could've added your short answer as well!!!! | |
| Jan 5, 2017 at 4:58 | comment | added | Michael R. Chernick | I'm voting to close this question as off-topic because it is too elementary and only requires a short answer. | |
| Jan 5, 2017 at 3:32 | review | Close votes | |||
| Jan 5, 2017 at 13:23 | |||||
| Jan 5, 2017 at 2:03 | review | First posts | |||
| Jan 5, 2017 at 5:03 | |||||
| Jan 5, 2017 at 2:02 | history | asked | lsadelaide | CC BY-SA 3.0 |