Re: st: Interpreting summarize, detail

[Phil Clayton <philclayton@internode.on.net>]

You need to read it in columns, not rows. There are 3 columns of results - percentiles, smallest and largest 4 values, and other stats.

So the smallest 4 values for mpg are 12, 12, 14 and 14; the largest 4 values for mpg are 34, 35, 35 and 41. These are completely separate from the percentiles which are in the first column of results.

Please also see the Statalist FAQ section 2.2 "please do not start a new thread by replying to a previous posting"

Phil

On 06/02/2011, at 5:09 PM, Syed Basher wrote:

> Hello all,
> 
> I am having some difficulty in interpreting the basic summary statistics. 
> Consider the following:
> 
> . sysuse auto
> (1978 Automobile Data)
> 
> . summarize mpg, detail
> 
>                       Mileage (mpg)
> -------------------------------------------------------------
>     Percentiles      Smallest
> 1%            12             12
> 5%            14             12
> 10%           14             14       Obs                       74
> 25%           18             14       Sum of Wgt.          74
> 
> 50%           20                           Mean            21.2973
>                             Largest       Std. Dev.       5.785503
> 75%           25             34
> 90%           29             35       Variance       33.47205
> 95%           34             35       Skewness    .9487176
> 99%           41             41       Kurtosis       3.975005
> 
> In the above output, the largest value in 75% is 34, while the starting value of 
> 90% is 29. The same is with the 95%. Why this is so? Shouldn't the percentile 
> value be monotonically increasing? I am interested in this because in one of my 
> own data, I have obtained the following output:
> 
>                          uprice
> -------------------------------------------------------------
>     Percentiles              Smallest
> 1%     .0022779         .0001697
> 5%       .01875          .0002087
> 10%     .0581161       .0010804       Obs                 626
> 25%     .3826962       .0012382       Sum of Wgt.     626
> 
> 50%     1.667209                                 Mean           79.44026
>                                  Largest             Std. Dev.      432.1083
> 75%     9.730152       2658.562
> 90%     77.09222       3077.629       Variance       186717.6
> 95%     363.5599       5423.877       Skewness       11.04796
> 99%      1490.65       7004.734       Kurtosis       151.0011
> 
> where as you can see I have a similar problem (largest value in 75% is much 
> higher than the starting value in 90% and so on). I am guessing that this is due 
> to the 3rd (skewness) and 4th (kurtosis) moments of the distribution. But I do 
> not have a convincing interpretation/explanation. Your help will be much 
> appreciated.
> 
> Regards,
> 
> Syed Basher
> Qatar National Food Security Programme
> 
> 
> 
> 
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