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- r - How to interpret a QQ plot? - Cross Validated
Since this thread has been deemed to be a definitive "how to interpret the normal q-q plot" StackExchange post, I would like to point readers to a nice, precise mathematical relationship between the normal q-q plot and the excess kurtosis statistic
- normal distribution - KL divergence between two univariate Gaussians . . .
I need to determine the KL-divergence between two Gaussians I am comparing my results to these, but I can't reproduce their result My result is obviously wrong, because the KL is not 0 for KL(p,
- Why do we assume that the error is normally distributed?
Due to the Central Limit Theorem, we may assume that there are lots of underlying facts affecting the process and the sum of these individual errors will tend to behave like in a zero mean normal distribution In practice, it seems to be so I'm interested in the second part actually
- How can I convert a lognormal distribution into a normal distribution?
8 I have a sample of data that follows a lognormal distribution I would like to represent the distribution as a "Gaussian" histogram and overlayed fit (along a logarithmic x-axis) instead of a lognormal representation For simplicity, I'll call the average and sigma of the lognormal data mu_log and sigma_log, respectively
- How are the Error Function and Standard Normal distribution function . . .
Well, there's a definition of erf and a definition of the Normal CDF The relations, derivable by some routine calculations, are shown as to how to convert between them, and how to convert between their inverses
- What is the difference between a Normal and a Gaussian Distribution
But the Normal distribution is the same as Gaussian which can be converted to a standard normal distribution by representing using the variable z = (x-mean) std
- normal distribution - why n gt;=30 for central limit theorem to hold . . .
From my understanding as size of n increase normal distribution will have smaller standard deviation, this makes sense because using larger sample size will be better at estimating population mean than smaller sample
- Pearsons or Spearmans correlation with non-normal data
It is also useful with ordinal data and is robust to outliers (unlike Pearson's correlation) The distribution of either correlation coefficient will depend on the underlying distribution, although both are asymptotically normal because of the central limit theorem
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