Linked Questions
10 questions linked to/from Why Normality assumption in linear regression
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Why can we suppose $\epsilon \sim \mathcal{N}(0,\sigma^2)$? [duplicate]
Suppose we want a regression of a function $f(x)$. Suppose $r = f(x) + \epsilon$. Why can we suppose that $\epsilon \sim \mathcal{N}(0,\sigma^2)$? What is the advantage of such supposition?
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What if residuals are normally distributed, but y is not?
I've got a weird question. Assume that you have a small sample where the dependent variable that you're going to analyze with a simple linear model is highly left skewed. Thus you assume that $u$ is ...
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Where does the misconception that Y must be normally distributed come from?
Seemingly reputable sources claim that the dependent variable must be normally distributed:
Model assumptions: $Y$ is normally distributed, errors are normally
distributed, $e_i \sim N(0,\sigma^2)...
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Statistical interpretation of Maximum Entropy Distribution
I have used the principle of maximum entropy to justify the use of several distributions in various settings; however, I have yet to be able to formulate a statistical, as opposed to information-...
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Importance of normal distribution
Why did the normal distribution become such a popular (important) distribution? I know one reason is because of CLT. Can you please give more reasons?
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Normality assumption in linear regression
As an assumption of linear regression, the normality of the distribution of the error is sometimes wrongly "extended" or interpreted as the need for normality of the y or x.
Is it possible to ...
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Assumption of normally distributed residuals in linear regression [duplicate]
Let us consider the simple linear model $y = \beta_0 + \beta_1 X + \epsilon$, where $y$ is real number, $X$ a matrix of reals and $\epsilon$ is the random "noise". The least-square estimate ...
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(Basic question) normal distribution of error in predictive model [duplicate]
When I am developing a predictive statistical model, why do I need to ensure the error is normally distributed? (I have a very small statistical background, so I apologize in advance if this is a very,...
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Linear regression: dependent variable is conditionally normal
I was reading about Maximum Likelihood and linear regression and found a lot of literature saying:
For a fixed Xi, the distribution of Yi is equal to N(f(Xi),σ2)
meaning the dependent variable is ...
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