Find distribution of the data from Q-Q plot - Cross Validated most recent 30 from stats.stackexchange.com 2019-09-21T19:48:22Z https://stats.stackexchange.com/feeds/question/91587 https://creativecommons.org/licenses/by-sa/4.0/rdf https://stats.stackexchange.com/q/91587 2 Find distribution of the data from Q-Q plot user21186 https://stats.stackexchange.com/users/0 2014-03-27T18:08:40Z 2014-04-11T17:37:18Z <p>With 10 thousands, Monte Carlo simulation, I have generated the Q-Q plot <img src="https://i.stack.imgur.com/1DPbO.png" alt="enter image description here"></p> <p>Is it possible to infer what distribution follows my sample?</p> <p>I'm new to Q-Q plot.<br> As far as I understand, the sample is not normal distributed because the dot are not on the line.<br> But, then, which distribution behave like that?</p> <p>This is related to this <a href="https://stats.stackexchange.com/q/91508/21186">question</a>.</p> <p><strong>Edit</strong><br> To give an idea of the data, here is the empirical cumulative distribution function of the sample <img src="https://i.stack.imgur.com/wI4FD.png" alt="enter image description here"></p> https://stats.stackexchange.com/questions/91587/-/93487#93487 0 Answer by PA6OTA for Find distribution of the data from Q-Q plot PA6OTA https://stats.stackexchange.com/users/43634 2014-04-11T17:28:42Z 2014-04-11T17:37:18Z <p>Judging from empirical CDF, this distribution is strange indeed. It simply looks like a value of 0 with 99.99% frequency, plus a smattering of large negative values. Is a lot of rounding involved? </p> <p>With the highly skewed (non-symmetrical) distribution like this, the first thing I'd do is to take log(-X), where X are your sample values. If there are a lot of zeros, then you deal with <strong>censoring</strong> which is a rather complicated topic and you'll need to talk to a professional. A simple practical fix is just skip the zeros for now and work with strictly negative part of your sample. </p> <p>EDIT: after looking at your original post, I notice that X is related to the stock price. IF this is a result of using some kind of geometric random walk (multiplying by some number at each step, for example), then it makes sense to do the entire simulation on the log scale (i.e. turn your multiplications into additions), then you will not get into the whole machine rounding situation and your X-values will be more meaningful.</p>