Running head: HISTORGRAMS AND DESCRIPTIVE STATISTICS
Histograms and Descriptive Statistics
Capella University
Katie Del Rio

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HISTOGRAMS AND DESCRIPTIVE STATISTICS2
Part 1: Histograms and Visual Interpretation
Skew
Skewness is “asymmetry in a statistical distribution, in which the curve appears distorted
or skewed either to the left or to the right” (Rajamanickam, 2001, p.58).
When looking at the
skewness on a graph it will appear as a bell-shaped curve, the mean, average and mode or the
maximum point on the curve, are equal (Warner, 2013).
There are ways to tell if it has a normal
distribution.
The tails on the curve (either side) have a mirror image of each other.
To tell is the
distribution is skewed, the tail on the left is a bit longer than the tail on the right, and the mean is
less than the mode (Warner, 2013).
In that case it is called negative skewness. And when the

HISTOGRAMS AND DESCRIPTIVE STATISTICS3
distribution is skewed towards the right the tail on the right side of the curve is longer than the
left side.
In that case it means that the mean is greater than the mode.
This is called positive
skewness.
The chart that is being used with the information provided for our current class the
skewness of the curve is considered positive, because the right side is longer than the right side.
Statistics
gender
N
Valid
105
Missing
0
Skewness
.456
Std. Error of
Skewness
.236

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HISTOGRAMS AND DESCRIPTIVE STATISTICS4
Kurtosis
Kurtosis is the statistical measure in which the distribution, or the skewness, of observed
data around the mean, sometimes referred to as the volatility or volatility.
This is used in
statistics in references to the graphs and charts.
“Kurtosis can be present in a chart with fat tails
and a low, even distribution, as well as be present in a chart with skinny tails and a distribution
concentrated toward the mean” (DeCarlo, 1997).
When looking at kurtosis it is important to
understand that it is “a measure that describes the shape of the distribution’s tail in relation to its
overall shape” (DeCarlo, 1997) rather than the peak of the distribution.

- Summer '16
- Staff