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help for ^skewplot6^
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Skewness plots
--------------

^skewplot6^ varname [^if^ exp] [^in^ range]
[^, skew^ graph_options ^by(^byvar^) mono miss^ing ]

^skewplot6^ varlist [^if^ exp] [^in^ range]
[^, skew^ graph_options ^mono miss^ing ]

Description
-----------

^skewplot6^ produces by default a plot of the midsummary versus the spread
for the variables in varlist, also known as the mid versus spread plot.

With the ^skew^ option, it produces a plot of the skewness function versus

Such plots convey both the general character and the fine structure of the
symmetry or skewness of data sets, and can be used to compare distributions
or to assess whether transformations are necessary or effective.

^skewplot6^ is a renamed clone of ^skewplot^ 2.0.0 which is for Stata 6
or Stata 7. Stata 8 users should use ^skewplot^ 3.0.0 or later.

Remarks
-------

Order n data values for a variable x and label them such that
x_(1) <= ... <= x_(n).

In a perfectly symmetric set of data, the midsummaries

(x_(1) + x_(n)) / 2,
(x_(2) + x_(n - 1)) / 2,
etc.

would all be identical, and equal to the median. A plot of each
midsummary

(x_(i) + x_(n - i + 1)) / 2

x_(n - i + 1) - x_(i)

would yield a horizontal straight line. Conversely, skewness in sets of
data will be reflected by departures from horizontality.

Apart from the divisor of 2, this plot was suggested by J.W. Tukey
Fisher (1983). The form used here and the name `mid versus spread plot'
are found in Hoaglin (1985). It is usual to plot only that half of the
sample results for which spread is >= 0.

The ^skew^ option produces an alternative form promoted by Benjamini and
Krieger (1996, 1999). The identity

x_(n - i + 1) = median

+ (x_(n - i + 1) - x_(i)) / 2

+ (x_(i) + x_(n - i + 1) - 2 * median) / 2

= median + spread function + skewness function

for x_(i) in the lower half of the sample leads to a plot of the skewness
plot. Note that the skewness function is midsummary - median, and will be
constant and zero for a perfectly symmetric distribution, and that the

x_(i) + x_(n - i + 1) - 2 * median
----------------------------------
x_(n - i + 1) - x_(i)

is a measure of skewness (in the traditional sense) originally suggested
for quartiles by Bowley (1902) and generalised to this form by David and
Johnson (1956). It varies between -1 and 1. A similar general measure was
used by Parzen (1979). Graphically this measure is the slope of the line
connecting (0,0) and each data point.

See Benjamini and Krieger (1996, 1999) and Groeneveld (1998) for concise
reviews tracing such ideas from late 19th century antecedents to recent
work and further details on the interpretation of the skewness versus

Options
-------

^skew^ specifies the skewness versus spread plot, not the default

graph_options are options allowed with ^graph, twoway^.

Note that with ^by( )^ each group is treated graphically as if it
were a separate variable, so long as the number of groups is not
greater than the limit in Stata on the number of y variables on a
scatter plot (20 in Stata 6.0).

With more groups, all functions must be treated graphically as a
single variable, by using the ^mono^ option, which enforces a
monochrome treatment. The only ^connect^ line style appropriate is
then ^c(L)^, and only one ^pen^ and point ^symbol^ may be used.

By default ^ysc( )^ and ^xsc( )^ show the extremes observed for each
variable. With the ^skew^ option, the maximum value of spread (smax,
say) can be used in a second pass with ^ysc(-^smax^,^smax^)^, which are
the limits on possible values for skewness. For example, if the
maximum value observed is ^12.34^, setting ^ysc(-12.34,12.34)^ gives a
y axis stretched to show extremes possible for skewness, corresponding
to limits for a Bowley-type measure of -1 and 1.

^by(^byvar^)^ specifies that calculations are to be carried out
separately for each group defined by ^byvar^. Any graph will,
however, show results for all groups. ^by( )^ is only allowed
with a single varname.

^mono^ specifies a monochrome treatment, with a single ^pen^ colour,
^connect^ style and point ^symbol^. See above, under graph_options.

^missing^, used only with ^by( )^, permits the use of non-missing values
of varname corresponding to missing values for the variable named by
^by( )^. The default is to ignore such values.

Examples
--------

. ^skewplot6 mpg^
. ^skewplot6 mpg, by(foreign) c(ll)^
. ^skewplot6 mpg, by(rep78) c(L) sy([rep78]) mono skew^
. ^skewplot6 length width height^

References
----------

Benjamini, Y. and Krieger, A.M. 1996. Concepts and measures for skewness
with data-analytic implications. Canadian Journal of Statistics 24:
131-140.

Benjamini, Y. and Krieger, A.M. 1999. Skewness -- concepts and measures.
In Kotz, S., Read, C.B. and Banks, D.L. (eds) Encyclopedia of Statistical
Sciences Update Volume 3. New York: John Wiley, 663-670.

Bowley, A.L. 1902. Elements of statistics. London: P.S. King.
(2nd edition: see p.331.)

David, F.N. and Johnson, N.L. 1956. Some tests of significance with
ordered variables. Journal, Royal Statistical Society B 18: 1-20.

Fisher, N.I. 1983. Graphical methods in nonparametric statistics: a review
and annotated bibliography. International Statistical Review 51: 25-58.

Gnanadesikan, R. 1977. Methods for statistical data analysis of multivariate
observations. New York: John Wiley.

Groeneveld, R. 1998. Skewness, Bowley's measures of. In Kotz, S., Read,
C.B. and Banks, D.L. (eds) Encyclopedia of Statistical Sciences Update
Volume 2. New York: John Wiley, 619-621.

Hoaglin, D.C. 1985. Using quantiles to study shape. In Hoaglin, D.C.,
Mosteller, F. and Tukey, J.W. (eds) Exploring data tables, trends, and
shapes. New York: John Wiley, 417-460.

Parzen, E. 1979. Nonparametric statistical data modeling. Journal, American
Statistical Association 74, 105-131.

Wilk, M.B. and Gnanadesikan, R. 1968. Probability plotting methods for
the analysis of data. Biometrika 55: 1-17.

Author
------

Nicholas J. Cox, University of Durham, U.K.
n.j.cox@@durham.ac.uk

Acknowledgments
---------------

Richard Groeneveld tracked down the Bowley reference.

Also see
--------

On-line: help for @graph@, @symplot@
Manual: [R] graph, [R] diagplots

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