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Chapter 7: Summarizing and Displaying Measurement (quantitative) Data
Top Ten Athlete Earnings 2009
Athlete
Tiger Woods
Phil Mickelson
LeBron James
Alex Rodriguez
Shaquille O'Neal
Kevin Garnett
Kobe Bryant
Allen Iverson
Derek Jeter
Peyton Manning
2009 Earnings
99737626
52950356
42410581
39000000
35000000
34750000
31262500
28937500
28500000
27000000
In Millions
99*
53
42
39
35
35
31
29
29
27
* Woods should round to 100 but for convenience of keeping all at two digits I
rounded down to 99.
Numbers to describe data:
- mode which is the most frequently observed data value (here there are two modes: 29 and
35)
- median and quartiles
- mean
The position of the median is found by (n+1)/2 which for our data set is (10+1)/2 = 5.5 which
represents that the median is located halfway between the 5th and 6th observation within the
ordered data. To calculate the median for our data we would take the midpoint of the 5th and 6th
observation or for this example the midpoint between 35 and 35 which is again 35. The
interpretation is that 50% of the top ten earnings were 35 million or less.
Similarly:
The position of the quartiles can be found by the calculating the median of the median! That is,
the position of the first quartile called Q1 is found by (n+1)/4 = (10+1)/4 = 2.75. For us this
makes the first quartile located halfway the second and third ranked salary which is 29. The
interpretation is that 25% of the top ten earnings were 29 million or less.
The position of the third quartile called Q3 is found by 3*(n+1)/4 = 3*(10+1)/4 = 8.25. For us
this makes the third quartile, Q3, located halfway between the 8th and 9th observation which
comes to halfway between 42 and 53. This makes the Q3 equal to 47.5. The interpretation is
that 75% of the top ten earnings were 47.5 million or less.
-
mean (sum of all values divided by the total number summed): mean = 41.9
-
variability measures:
i. range (max – min = 99 – 27 = 72),
ii. Interquartile Range or IQR = Q3 – Q1 = 47.5 – 29 = 18.5 The IQR represents how spread
out the middle 50% of the observations are since this measure is the difference between the third
and first quartile (i.e. 75% - 25% = 50%)
iii.variance and standard deviation (or SD or Std. Dev.). These two are measures of “how
spread out the values are from the mean” and are related: standard deviation is the square root of
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the variance. Not important to know how to calculate these but to know their meaning. A basic
interpretation of the standard deviation is that it is roughly the average distance of the observed
values from their mean. In our example the SD is 21.5 making the variance 21.52 = 462.25
-
Five number summary: min (27), Q1 (29), Median (35), Q3 (47.5) and max (99)
NOTE: Sometimes these values are not possible outcomes (e.g. the mean number of children in
a US household is 2.2) we do NOT round the number to a whole number (e.g. we would not
round this to 2). The value is important as it tells us that on average his mean number of children
is less than 3 but more than 2.
Graphing measurement data and shape:
- stem and leaf
- histogram
- boxplot
- symmetric (or bell shaped), skewed, and outliers
Stem-and-Leaf Display: In Millions
2
3
4
5
6
7
8
9
799
1559
2
3
9
Histogram of In Millions
3.0
Frequency
2.5
2.0
1.5
1.0
0.5
0.0
30
40
50
60
70
In Millions
80
90
100
This histogram would be interpreted as right-skewed or positively skewed since the extreme
observations are “pulling” or “stretching” the data to the right or in a positive direction. For such
distributions we would expect the mean to be more than, or to the right, of the median. This is
the case for this example as the mean of 41.9 is greater than the median of 35.
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Building a “fence” around the data to determine extreme observations or outliers.
We can use the quartiles and IQR to build a fence around our data in order to determine if any
observations in our data set can be considered extreme or outliers. This fence is built by:
Lower Fence: Q1 - 1.5*IQR
Upper Fence: Q3 + 1.5*IQR
For this data set with Q1 of 29, Q3 of 47.5, and IQR of 18.5 the fence is:
Lower: 29 - 1.5*18.5 = 29 – 27.75 = 1.25
Upper: 44.75 + 1.5*18.5 = 47.5 + 27.75 = 75.25
Looking at the data we see only one value that does not lie within this “fence”: Tiger Woods
with 99 million dollars. Therefore, for this data set 99 would be classified as an outlier.
Creating a Box Plot
A box plot is simply a representation of the median and the quartiles representing a “box” and
“whiskers” representing the fence. This plot is sometimes referred to as a box-and-whisker plot.
Below is a box plot our data. The box itself consists of Q1 and Q3 with the line within the box
being the median (note how the big box goes from 29 to 44.75, the Q1 and Q3, with the line
within this box being at 35 or the median). The whiskers extend out both sides of the box but
only extend to the observed value closest to the lower and upper fence without exceeding these
values. For example, the lower fence is at 1.25 but the closest observation to this value without
going below it is 27; thus the whisker stops at 27. The other whisker goes to 53 since this is the
closest observed value that does not exceed the upper fence of 75.25
Boxplot of In Millions
20
30
40
50
60
In Millions
70
80
90
100
The effect of these extreme observations, also called outliers, is greatest on the mean and
standard deviation/variance since the latter uses the mean in its calculation. This effect is
due to the mean taking into account the values of all observations meaning that Tiger
Woods earnings has a greater impact on the mean than it does the median (and thus less
of an impact on the quartiles and IQR). The range, too, is greatly affected by outliers
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Empirical Rule
When the data is symmetric or bell-shaped the use of SD is quite helpful. You would find in such
instances that for data shaped this way that roughly 68% of the observations fall with +/- one SD
from the mean; 95% of the observations fall within +/- two standard deviations from the mean;
and almost all – 99.7% of all observations fall within +/- three standard deviations from the mean.
For example the math portion of the SAT test is typically bell-shaped with a mean of 500 and
standard deviation of 100. Thus we would expect that 68% of all scores would be between 400
and 600; 95% would fall between 300 and 700; and almost all (99.7%) would fall between 200
and 800.
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