```Measures of Spread
(or Variability or Dispersion)
1
Dispersion
Dispersion refers to how much the data are spread out.
Analogy: In terms of physical fitness, a person which can do
the “splits” is more agile than one who can not. The agile one
Data sets that are more disperse are spread out more.
Other names for dispersion are variability, variation or spread.
So, when we look at a variable we can look at the variation.
This is the amount of scattering of the values away from the
central value.
2
The Range
The range is a measure of dispersion and can be found by
largest value on a variable minus the smallest value.
For example the range of the data set 1, 3, 5 is 5 minus 1 = 4.
The range as a measure of variability has a problem in that
the lowest and the highest numbers could be far away from
the rest of the data. This would suggest more variability than
perhaps there really is in the data.
Next I want to look at the interquartile range as a measure of
3
Percentiles
A percentile is a measure of
relative standing, meaning we get
a value relative to the rest of the
data set.
4
4
An example: last 10 golf scores for 18 holes, sorted:
82, 83, 84, 85, 85, 88, 90, 90, 93, 95
1
2
3
4
5
6
7
8 9 10
Here I have an example where I wrote down the last 10 golf
scores I had. Note that I have sorted the scores from low to high
score (although that is not the order I shot them – but for what I
want to do next I need to have the data sorted from low to high –
ascending order).
Below each score I put the “location” of the score in the
ascending order.
5
5
Here is an approximation method to get percentiles.
Let p = the percentile of interest,
n = the number of data points or observations, then
Lp = (n + 1)(p/100)n is a location index number (a fancy name
for a handy little device) we will use to find pth percentile.
Now, if n = 10 and if we want the 25th percentile the location
index is
Lp = (10 + 1)(25/100) = 11(.25) = 2.75.
In my example data points have locations with whole numbers.
The location index of 2.75 is between 2 and 3. The 25th
percentile value will be 75% of the way between the 2nd and 3rd
number.
6
6
The 2nd number is 83 and the third number is 84. To get the 25th
percentile number take the lower number, 83 and add .75 of the
difference between the 2nd and 3rd numbers:
83 + .75(84 – 83) = 83.75.
Note if the index is a whole number the value in that location is
the percentile of interest.
The 50th percentile here is found by:
Lp = (10 + 1)(50/100) = 5.5 or half the way between the 5th and
6th numbers. So,
85 + .5(88 – 85) = 86.5 is the 50th percentile.
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7
Quartiles
Quartiles are just special percentiles. The 25th percentile is
the 1st quartile, the 50th percentile is the 2nd quartile (and also
called median) and the 75th percentile is the 3rd quartile.
What is most important here, I think, is that you understand
the meaning of a percentile and the special percentiles called
quartiles.
So the meaning of the ith percentile is that in the data set that
has been arranged from low to high value the ith percentile is
the value such that i percent of the cases have this value or
less! In my golf score example, the 25th percentile of 83.75
means that out of my last 10 scores 25 percent or fewer of the
scores were less than 83.75.
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8
special percentiles
•The 25th and 75th percentiles are called the 1st and 3rd
quartiles(Q1 and Q3), respectively. They are just the
medians of the lower and upper halves of the arranged
values.
The following is a visual to see the percentiles.
lowest
25%
of observations
first quartile is
a value
next
25%
next
25%
median is a value
highest
25%
third
quartile is
a value
number line
where we
measure
values
of the
variable
9
Interquartile range
•Variation can be indicated by the interquartile range,
IQR = Q3 - Q1. The smaller the IQR, the closer Q3 and Q1
are in the graph and thus the lower the spread!
•Hey, please check out the box plot, or what is sometimes
called the box and whiskers plot in the text. I know you
will! This plot is based on the 5 number summary.
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
standard deviation
The standard deviation is, perhaps, the most
 It
is related to the concept called the
variance in that the standard deviation
is the square root of the variance.

Example: What is the square root of 9? 3 - no biggie!
 The
standard deviation is used so much
because it is useful in visually
understanding the normal distribution.
We will see this later.
11
On the next screen I have pointed out an example and I will use
the following notation: xi xi - x (xi - x)2
xi is just the ith data value, x is the mean of the data, and
(xi - x)2 is the mean subtracted from a data value and then squared.
As an example, say the a data value is 6 and the mean of the data is
7. Then we would have (6 – 7 )2 = (-1)2 = 1
A deviation is a data value minus the mean. If you think about it, a
deviation is just a distance on the number line. 6 – 7 = -1 means 6
is one unit away from the mean, and the minus sign means on the
low side of the mean.
(6 – 7 )2 is just a deviation squared, or a squared deviation.
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standard deviation
•Let’s do some simple examples I have made up to see
what is going on.
•Note below we have three observations, the values of x
are 6, 7, 8 and the average of the three numbers is 7.
obs
1
2
3
xi
6
7
8
xi - x
6-7
7-7
8-7
(xi - x)2
1
0
1
Σ(xi - x)2 = 2
The sum of the
squared deviations.
So the variance is 2/2 = 1 (I show how in a few slides),
where the denominator
is n-1 (the number of numbers minus 1).
The standard deviation is thus sqrt(1) = 1.
13
standard deviation
•Here is another simple example. Note below we have
three observations, the values of x are 5, 7, 9 and the
average of the three numbers is 7.
•The previous example had numbers 6, 7, 8, numbers not
spread out as much on the number line. We will see
obs
1
2
3
xi
5
7
9
xi - x (xi - x)2
the numbers 5, 7, 9 have
5-7
4
a larger standard
7-7
0
deviation.
9-7
4
Σ(xi - x)2 = 8
So the variance is 8/2 = 4.
The standard deviation is thus sqrt(4) = 2.
14
 Both
standard deviation notes
examples have sample mean of 7.
 The first example has values closer to 7
and it had the smaller calculated
standard deviation.
 So, the closer the values of the variable
are to the mean, the smaller is the
standard deviation - the smaller the
15
Variances and Standard Deviations
Remember we want to think about variability here. Variance and
standard deviation are related in that the standard deviation is the
square root of the variance. How do we interpret these concepts.
At this point I think we need to just put them in the context of two
data sets. The data set with a larger variance (or standard
deviation) will be the one that is more spread out – has more
variability.
Remember: Data set a = 6, 7, 8. Data set b = 5, 7, 9 By the
variance measure data set b is more spread out.
By the way, in the variance and standard deviation calculations the
sum of the squares of the deviations of the data values from the
mean is often just called the sum of squares – SS.
16
Population and Sample
The population variance and standard deviations are based on
adding the squared deviations. In fact, the variance is just the
average of the squared deviations. In symbols, the population
variance is
σ2 = Σ(xi – μ)2/N
The sample variance is similar,
s2 = Σ(xi – x)2/(n-1).
Remember N = population size, n = sample size. Note in the
sample variance there is division by n-1. Why? Later when we do
inference procedures dividing by n-1 makes the resulting sample
variance a better way to estimate the population variance.
17
The Coefficient of Variation
By definition, the coefficient of variation is
(standard deviation/mean)100.
Let’s think about an example of the monthly salary of some recent
graduates. Say the mean is \$2940 and the standard deviation is
165.65. Then the coefficient of variation is
(165.65/2940)100 = 5.6 Thus, the sample standard deviation is only
5.6% of the sample mean.
Why even have this crazy measure? This is a useful measure when
comparing the variability of variables that have different standard
deviations and different means.
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