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Shape of Histograms
Figure 3.2 Three Symmetric Histograms
1.Symmetric histogram
A symmetric distribution is one in which the 2 "halves" of the histogram appear as
mirror-images of one another.
The different types of a histogram are symmetric histogram, bimodal
histogram, probability histogram.uniform histogram,
1.1 Uniform Histogram
A uniform shaped histogram indicates data that is very consistent; the frequency
of each class is very similar to that of the others. A data set with a uniform-
shaped histogram may be multimodal having multiple intervals with the
maximum frequency. One indication of a uniform distribution is that the data may
not be split into enough separate intervals or classes.
Another possibility is that the scale of the histogram may need to be adjusted in
order to offer meaningful observations.
1.2 . Undefined Bimodal
This shape is not specifically defined, but we can note regardless that it is bi-
modal, having two separated classes or intervals equally representing the
maximum frequency of the distribution.
1.3. Skewed histogram
For skewed distributions, it is quite common to have one tail of the distribution
considerably longer or drawn out relative to the other tail.
A "skewed right" distribution is one in which the tail is on the right side.
A "skewed left" distribution is one in which the tail is on the left side. The above
histogram is for a distribution that is skewed right.
Skewed distributions bring a certain philosophical complexity to the very process
of estimating a "typical value" for the distribution.
example ; To be specific, suppose that the analyst has a collection of 100 values
randomly drawn from a distribution, and wishes to summarize these 100
observations by a "typical value".
1.3.1. Some Causes for Skewed Data
Skewed data often occur due to lower or upper bounds on the data. That is, data
that have a lower bound are often skewed right while data that have an upper
bound are often skewed left. Skewness can also result from start-up effects. For
example, in reliability applications some processes may have a large number of
initial failures that could cause left skewness. On the other hand, a reliability
process could have a long start-up period where failures are rare resulting in
right-skewed data.

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