MORE: The Ultimate Social Media Dictionary: 35 Phrases and What They Mean Well, as it happens, POIDH stands for “Pictures, Or It Didn’t Happen”-one of dozens of acronyms that have cropped up as the digital age forces people to embrace brevity-be it 140 character or fewer-in their quest to communicate. You’re cruising through your Twitter or Instagram feed, reading dispatches from folks you follow, when suddenly you come across an acronym you’ve never seen before. You could just make the prediction intervals wider, or you could again model it, especially if the subgroups are identifiable.It’s happened to all of us. So long as you don't collapse the global financial system, it might not be so bad. For purposes of prediction, mean predictions should be unaffected by this, but prediction intervals based on normality will be incorrect and yield ' black swans' and occasionally cause problems. It may well be that the heteroscedasticity can be easily found and yield meaningful insights into your data.Īs noted, I wouldn't worry about this for purposes of statistical inference, although if you can identify a heterogeneous subgroup, you can model your data using weighted least squares. You might also simply make some boxplots of your residuals as a function of your categorical variables, either individually or in specified combinations.
#Qq meaning in text how to#
I demonstrate a basic analysis in my answer to How to test if my distribution is multimodal?
#Qq meaning in text software#
In R, that can be done with the Mclust package, although any decent statistical software should be able to do it. Rnorm( 400, mean=0, sd=s)) # small fraction comes from 2nd dist w/ greater SDĪ better way to determine the mixing proportions and relative SDs would be to fit a Gaussian mixture model. X = c(rnorm(11600, mean=0, sd=1), # 99.7% of the data come from the 1st distribution I can generate a plot that looks pretty similar to yours pretty easily in R with the following code: set.seed(646) # this makes the example exactly reproducible That suggests you have a mixture of two distributions with the same mean, but different standard deviations. Your example is notable in that the middle is very straight, and the ends are also very straight and roughly parallel to each other, with fairly sharp corners in between. I would often start by looking at $t$-distributions, because they are well understood, and you can adjust the tail 'fatness' by modulating the degrees of freedom parameter.
There are lots of distributions that are symmetrical and have fatter tails than the normal. In other words, those points are much further from the mean than you would expect if the data generating process were actually a normal distribution. Given that sample quantiles (i.e., your data) are on the y-axis, and theoretical quantiles from a standard normal are on the x-axis, that means the tails of your distribution are fatter than what you would see from a true normal.
Namely, the ends of the line of points turn counter-clockwise relative to the middle. The set of examples in How to interpret a QQ plot includes the basic shape in your question.
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