PROPHET StatGuide: Examining F test results to detect assumption violations
All the following results are provided as part of a PROPHET F test for variance.
- Normality tests:
- If the assumptions for the F test hold, the values from
each sample should come from a
normal distribution.
Departures from normality can suggest the presence of
outliers
in the data, or of a nonnormal distribution
in one or more of the samples.
The normality test will give an indication of whether the
populations
from which the samples were drawn
appear to be normally distributed, but will not indicate the cause(s)
of the nonnormality. The smaller the sample size, the less
likely the normality test will be able to detect
nonnormality.
- Histograms:
- The histogram
for each sample has a reference
normal distribution
curve for a normal distribution with the same mean and variance
as the sample. This provides a reference for detecting gross
nonnormality when the sample sizes are large.
- Boxplots:
- Suspected
outliers
appear in a
boxplot
as individual points o or x outside
the box. If these appear on both sides of the box, they also suggest the
possibility of a
heavy-tailed
distribution. If they appear on only one side,
they also suggest the possibility of a
skewed
distribution. Skewness is also
suggested if the mean (+) does not lie on or near the central line of the
boxplot, or if the central line of the boxplot does not evenly divide the box.
Examples of these plots
will help illustrate the various situations.
- Normal probability plot:
- For values sampled from a
normal distribution,
the
normal probability plot,
(normal Q-Q plot)
has the points all lying on or near the straight line drawn
through the middle half of the points. Scattered points
lying away from the line are suspected
outliers.
Examples of these plots
will help illustrate the various situations.
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Last modified: March 14, 1997
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