Timeline for What is a complete list of the usual assumptions for linear regression?
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13 events
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| Nov 21, 2018 at 6:20 | comment | added | Parthiban Rajendran | Also if you make $\mathbf X$ as fixed and known, then in PRF, $E(Y|x)$ becomes simply $E(Y)$? | |
| Nov 21, 2018 at 5:59 | comment | added | Parthiban Rajendran | So for regression of $\mathbf Y$ with $X$, $\mathbf X$ is not a Random Variable? (This would also indirectly assert why slope estimator turns out to be normal because its linear combination of $\mathbf Y$, so want to confirm). Screenshot | |
| Mar 26, 2018 at 14:51 | comment | added | gung - Reinstate Monica | @AdamO, consider, eg, this situation: Choosing between LM and GLM for a log-transformed response variable, & my answer there. | |
| Mar 26, 2018 at 14:33 | comment | added | AdamO | What does this give you? In what sense is it an assumption? For reproducibility, that assumption relaxes the "linearity" (i.e. that the mean model is true) I think we require that the $X$ are obtained from the same probability model. They need not be exactly the same. | |
| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
replaced http://stats.stackexchange.com/ with https://stats.stackexchange.com/
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| Apr 14, 2015 at 14:13 | comment | added | gung - Reinstate Monica | @user1205901, the top model is of the data generating process, the bottom is your estimate of it. | |
| Apr 14, 2015 at 8:41 | comment | added | user1205901 - Слава Україні | Why do the βs and the ε have a hat in the bottom equation, but not in the top one? | |
| Apr 4, 2013 at 21:18 | history | edited | gung - Reinstate Monica | CC BY-SA 3.0 |
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| Dec 9, 2012 at 16:00 | comment | added | gung - Reinstate Monica | W/ predictive modeling, that's not quite true, but we will treat our $X$ data that way in the future, when we use the model to make predictions. | |
| Dec 9, 2012 at 16:00 | comment | added | gung - Reinstate Monica | @stan, I recognize your confusion. Terminology in stats is often confusing & unhelpful. In this case, "fixed" is not quite the same as the fixed in 'fixed effects & random effects' (although they are related). Here, we're not talking about effects--we're talking about the $X$ data, ie your predictor / explanatory variables. The easiest way to understand the idea of your $X$ data being fixed is to think of a planned experiment. Before you have done anything, when you're designing the experiment, you decide what the levels of your explanatory will be, you don't discover them along the way. | |
| Dec 9, 2012 at 10:22 | comment | added | abc | What does it mean "fixed" | "random" in plain language? And how to distinguish between fixed and random effects(=factors)? I think that in my design there is 1 fixed known factor with 5 levels. Right? | |
| Dec 5, 2012 at 14:21 | history | edited | gung - Reinstate Monica | CC BY-SA 3.0 |
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| Dec 5, 2012 at 5:27 | history | answered | gung - Reinstate Monica | CC BY-SA 3.0 |
