[R] Website, book, paper, etc. that shows example plots of distributions?

davidr at rhotrading.com davidr at rhotrading.com
Fri Feb 13 19:23:18 CET 2009


Jason,
Just to answer your direct question, there is Mathowrld.wolfram.com,
where there are 87 continuous distributions listed.
I have also used the book Statistical Distributions, 2nd ed, Merran Evans, et al.
which has most of the usual distributions with pictures and relationships.

Of course all of the advice about really thinking about what you are trying to accomplish is right on target.
HTH,
-- David


-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Jason Rupert
Sent: Friday, February 13, 2009 7:12 AM
To: Gabor Grothendieck
Cc: R-help at r-project.org
Subject: Re: [R] Website, book, paper,etc. that shows example plots of distributions?

Thank you very much.  Thank you again regarding the suggestion below.  I will give that a shot and I guess I've got my work counted out for me.  I counted 45 different distributions.  

Is the best way to get a QQPlot of each, to run through producing a data set for each distribution and then using the qqplot function to get a QQplot of the distribution and then compare it with my data distribution? 

As you can tell I am not a trained statistician, so any guidance or suggested further reading is greatly appreciated.  

I guess I am pretty sure my data is not a normal distribution due to doing some of the empirical "Goodness of Fit" tests and comparing the QQplot of my data against the QQPlot of a normal distribution with the same number of points.  I guess the next step is to figure out which distribution my data most closely matches.  

Also, I guess I could also fool around and take the log, sqrt, etc. of my data and see if it will then more closely resemble a normal distribution.   

Thank you again for assisting this novice data analyst who is trying to gain a better understanding of the techniques using this powerful software package.  




--- On Fri, 2/13/09, Gabor Grothendieck <ggrothendieck at gmail.com> wrote:
From: Gabor Grothendieck <ggrothendieck at gmail.com>
Subject: Re: [R] Website, book, paper, etc. that shows example plots of  distributions?
To: jasonkrupert at yahoo.com
Cc: R-help at r-project.org
Date: Friday, February 13, 2009, 5:43 AM

You can readily create a dynamic display for using qqplot and similar functions
in conjunction with either the playwith or TeachingDemos packages.

For example, to investigate the effect of the shape parameter in the skew
normal distribution on its qqplot relative to the normal distribution:

   library(playwith)
   library(sn)
   playwith(qqnorm(rsn(100, shape = shape)),
       parameters = list(shape = seq(-3, 3, .1)))

Now move the slider located at the bottom of the window that
appears and watch the plot change in response to changing
the shape value.

You can find more distributions here:
http://cran.r-project.org/web/views/Distributions.html

On Thu, Feb 12, 2009 at 1:04 PM, Jason Rupert <jasonkrupert at yahoo.com>
wrote:
> By any chance is any one aware of a website, book, paper, etc. or
combinations of those sources that show plots of different distributions?
>
> After reading a pretty good whitepaper I became aware of the benefit of I
the benefit of doing Q-Q plots and histograms to help assess a distribution.  
The whitepaper is called:
> "Univariate Analysis and Normality Test Using SAS, Stata, and
SPSS*" , (c) 2002-2008 The Trustees of Indiana University Univariate
Analysis and Normality Test: 1, Hun Myoung Park
>
> Unfortunately the white paper does not provide an extensive amount of
example distributions plotted using Q-Q plots and histograms, so I am curious if
there is a "portfolio"-type  website or other whitepaper shows
examples of various types of distributions.
>
> It would be helpful to see a bunch of Q-Q plots and their associated
histograms to get an idea of how the distribution looks in comparison against
the Gaussian.
>
> I think seeing the plot really helps.
>
> Thank you for any insights.
>
>
>
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>
>
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