On 14 Feb, 18:03, "David Biddulph" wrote:
What do you mean by "the error values"? What formula are you using? If you
are merely changing the standard deviation of the distribution, this won't
necessarily change the Kurtosis.
--
David Biddulph

"Will" wrote in message

oups.com...



I am trying to use the Kurtosis functions to examine the distribution
of a set of errors (forecast - actual). I am comparing the K values to
a histogram of the data.

To test I have deliberately peaked and flattened the distribution by
reducing the error values by 10%, 50% and 90% and increasing the error
values by 200%. The resulting histograms show the changes in
distribution for each adjusted set as you would expect but I am seeing
no change in the corresponding kurtosis values.

Anyone any ideas why this might be?- Hide quoted text -

- Show quoted text -

Thanks for your response.

I'm using forecast minus actual for each case to give a set of net
error readings. There are approx 24,000 readings. I am then applying
the KURT function to the set of net errors and also producing a
histogram of the data.

To vary the distribution I am reducing or increasing those values by a
proportional amount. For example to reduce the net error by 10% I am
taking the original value and multiplying by 0.9. I am applying this
same formula across the entire set of readings and the histogram
changes shape accordingly - when I double the error values I get a
flatter profile with thicker tails, as I reduce the error values I get
a more peaked distribution. Would you not expect the kurtosis value to
change accordingly?

By the way, there is a negative skew to the data i.e. the majority of
the readings are to the positive side of the histogram. Also I ran
KURT and SKEW functions over subsets of the data and that yields
different kurtosis and skewness values for each subset.

I'm a bit new to this - I basically stumbled across kurtosis and
skewness as a possible way of describing the accuracy of a set of
forecasts. We have run into issues with MAPE and other statistical
measures and these functions (in conjunction with histograms to
provide visual indication) would seem to be a useful way of describing
not only the magnitude of error but also the direction in terms of
tendency to over- or under-forecast.