Quartiles in the Peltier Tech Chart Utility

This is the fifth of a five part series.

Quartiles for Box Plots

This topic is covered in the companion page Quartiles for Box Plots.

Hinge Techniques for Determining Quartiles

This topic is covered in the companion page Hinges.

Interpolation Methods of Determining Quartiles

This topic is covered in the companion page Quartiles.

Comparison of Values from All Hinge and Quartile Methods

This topic is covered in the companion page Comparison.

Quartiles in the Peltier Tech Chart Utility

The Peltier Tech Chart Utility provides a number of quartile methods, including Tukey’s Inclusive Hinge method, the Moore and McCabe Exclusive Hinge method, the CDF hinge technique used by default in SAS, the (N+1) Basis Interpolation approach used by Minitab and several other packages, the (N) Basis technique, and the (N-1) calculation used in Excel. The CDF calculation is selected by default, but this option can be changed in the simple listbox, inserted in the worksheet next to the box plot.

Quartile Options available in Peltier Tech Chart Utility

Peltier Tech Chart Utility

Comparison of Values from All Hinge and Quartile Methods

This is the fourth of a five part series.

Quartiles for Box Plots

This topic is covered in the companion page Quartiles for Box Plots.

Hinge Techniques for Determining Quartiles

This topic is covered in the companion page Hinges.

Interpolation Methods of Determining Quartiles

This topic is covered in the companion page Quartiles.

Comparison of Values from All Hinge and Quartile Methods

Effect of N

First and third quartiles (or first and second hinges) for N=8 through 15 are tabulated below for all of the quartile determination methods described in the previous sections.

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Here the hinges for the Tukey (inclusionary) and the Moore and McCabe (exclusionary) methods are plotted. We see that for even N, the methods result in the same hinges, while for odd N, Tukey is closer to the median, and M&M is further from the median.

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Here the CDF is overlaid on the previous plot of Tukey and M&M. For even N, all techniques agree, while for odd N, the CDF sticks with the method that yields a whole number index.

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This chart plots the quartile indices for the N+1, N, and N-1 Basis approaches. The N-1 quartiles are closer to the median, the N+1 quartiles are further, and the N are in between. This is the pattern we noticed in the number lines in the previous section.

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It becomes interesting when we overlay the various hinge techniques on the N+1/N/N-1 plot. We see that the Tukey hinges is bounded by the N-1 and N quartiles.

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The M&M quartiles are bounded by the N and N+1 quartiles.

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And the CDF hinges are bounded by N+1 and N-1.

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Finally, since these quartiles are intended for use in box plots, here are box plots comparing the six techniques, one box plot each for N=8, 9, 10, and 11.

Box Plot showing variation in quartile definitions (N=8)

Box Plot showing variation in quartile definitions (N=9)

Box Plot showing variation in quartile definitions (N=10)

Box Plot showing variation in quartile definitions (N=11)

Doubling the Data Set

Before making any recommendations, let’s see how the techniques compare when we double a data set. For example, if we have a data set of {1,2,3,4,5} and another data set with the same values, but two of each, {1,1,2,2,3,3,4,4,5,5}, we would expect to find the same quartiles. Here is what all the techniques predict.

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Forget staring at a table of numbers, the predictions are plotted in the following charts. Any pairs that are not vertically aligned have different quartiles for the data set and its double. These unmatched cases are drawn in orange in the charts below and also in the table above.

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Of all the techniques evaluated, only CDF yields the same quartiles for all cases of a data set and its double.

Techniques Used by Software Packages

The following chart rehashes the difference between the N+1 and N-1 techniques for interpolating quartiles. Microsoft Excel’s legacy QUARTILE function uses the N-1 approach, while Minitab, JMP, and other packages use the N+1 approach. Microsoft added two functions to Excel 2010: QUARTILE.INC, which is based on N-1 and is therefore identical to QUARTILE, and QUARTILE.EXC, which is based on N+1. SAS also offers an N+1 option (see below).

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The quartiles for the N-1 technique are closer to the median, so the interquartile range (IQR) is smaller, and the limit for identifying outliers, 1.5IQR above Q3 and below Q1, are also closer to the median.

This means that there are likely to be more outliers identified by Excel than by Minitab. This difference in behavior was a mystery to me a decade ago when my employer provided us with Minitab in addition to Excel, but now it’s very clear. (Many more things were a mystery to the brilliant minds I was stuck working with, but that’s a story for another day, when we’re killing time and beers in the pub.)

The next chart shows the two SAS quartile options. The default is CDF (SAS option PCTLDEF = 5), which as we have seen yields identical quartiles for a data set and the same data set with two of each value. SAS also offers the N+1 option (PCTLDEF = 4), which is used by Minitab, JMP, and Excel’s QUARTILE.EXC. SAS also offers three more options (PCTLDEF 1, 2, and 3), which often produce asymmetric median and quartile definitions, because they round to the larger or closer of two values instead of averaging.

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The two sets of results are slightly different, and the CDF quartiles tend to be closer to the median than the N+1 quartiles. As with Excel’s N-1 results, the CDF will have smaller IQR than N+1, leading to identification of more borderline outliers.

Recommendations

So after all this noise, which quartile definition should you use?

The CDF approach is considered by Langford (Quartiles in Elementary Statistics) to be the all-around “best” approach. It is also the default for the powerful software package SAS, though it doesn’t seem to be used in other packages. This may then be the option of choice.

However, an important consideration is consistency. If others you work with are using Minitab or JMP, you should use the N+1 option for compatibility.

Quartiles in the Peltier Tech Chart Utility

This topic is covered in the companion page Quartiles in the Peltier Tech Chart Utility.

Peltier Tech Chart Utility

Interpolation Methods of Determining Quartiles

This is the third of a five part series.

Quartiles for Box Plots

This topic is covered in the companion page Quartiles for Box Plots.

Hinge Techniques for Determining Quartiles

This topic is covered in the companion page Hinges.

Interpolation Methods of Determining Quartiles

As in the case of hinges, we need to consider four cases:

N = 4k
N = 4k + 1
N = 4k + 2
N = 4k + 3

We will look at 8 (4k), 9 (4k+1), 10 (4k+2), and 11 (4k+3) values. Conceptually they are the same, but the simplified equations have differences.

We also need to look at three ways to distribute values along a number line. I’ll refer to these as the (N-1) Basis, the (N) Basis, and the (N+1) Basis. They are similar approaches, and give similar results.

N-1, N, and N+1 Bases for Computing Quartiles

To make it easier to explain and comprehend the discussions that follow, the data sets will be laid out along a number line such as this:

Number line

The gray numbers under the number line correspond to locations along the length of the set of values (as a continuous variable), while numbers above the number line correspond to the index of a particular value of the data set. A fractional number above the number line indicates that the resulting value is interpolated between the adjacent values.

(N-1) Basis

The values are laid out on the integer positions of the number line, starting at zero and ending with N-1. The N values are distributed over a length of  N-1. For N=8, it looks like this:

(N-1) Basis for 8 observations

The position of a percentile p is that percentage of the length of the number line, or p*(n-1).

The N-1 Basis method is used by Excel’s legacy QUARTILE function and by the QUARTILE.INC function introduced in Excel 2010. It is apparently not used in any other calculation packages, though it is equivalent to at least one obscure quartile determination method not discussed here.

(N) Basis

Each value is located in the middle of a one-unit length of number line. Value 1 is located midway between 0 and 1, etc. The number line starts at zero and ends at N. The N values are distributed over a length of N. Again, for N=8:

(N) Basis for 8 observations

The position of a percentile p is that percentage of the length of the number line, or p*(n).

The N Basis method is apparently not used by any package, even though it seems to me to be the most balanced method (while the others seem to have one kind of off-by-one error or the other).

(N+1) Basis

The values are laid out on the integer positions of the number line starting at 1 and ending at N. The number line extends beyond the first value to zero, and for symmetry beyond the Nth value to N+1. The values are then distributed over a length of N+1. For N=8:

(N+1) Basis for 8 observations

The N+1 Basis is used by Minitab and JPM, and is available in SAS. It is also used in the QUARTILE.EXC function introduced in Excel 2010.

8 (4k) Values

The formulas for locating the quartiles on the number line for N=8(4k) using the N-1 Basis are shown here:

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We need to increase the number line indices by 1 to get the value indices (or observation numbers).

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These value indices are not the values of the quartiles. The value indices indicate which value is to be used as the quartile. A whole number M indicates that the Mth value is the quartile. A fractional number of the form M+m means the quartile is located between the Mth and (M+1)th values, the fraction m away from the Mth value.

For example, let’s look at the 8-observation data set {2,3,5,8,11,12,14,17} in the chart below. For N=8 using the N-1 basis, our quartiles are the 2.75th, 4.5th, and 6.25th values. For the first quartile, 2.75 means the value 0.75 of the way from the 2nd to the 3rd values, or 0.75 of the way from 3 to 5, or 4.5. For the median, 4.5 means halfway between the 4th and 5th values, or halfway between 8 and 11, or 9.5. For the third quartile, 6.25 means the value 0.25 of the way from the 6th to the 7th value, or 0.25 of the way from 12 to 14, or 12.5.

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Locate the quartiles on the number line for N=8(4k) using the N basis using these formulas:

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These number line indices must be incremented by 0.5 to get the value indices.

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Looking at the 8-observation data set using the N basis, the quartiles are the 2.5th, 4.5th and 6.5th values, or 4, 9.5, and 13.

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Finally, for N=8(4k) and the N+1 Basis, the number line indices are computed using:

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The number line indices correspond exactly to the value indices.

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Looking at the 8-observation data set using the N+ basis, the quartiles are the 2.25th, 4.5th and 6.75th values, or 3.5, 9.5, and 13.5

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The pattern you should notice is that while the medians are the same, the N-1 Basis quartiles are closer to the median than the N+1 Basis quartiles, and the N Basis quartiles are in between these. This chart compares the N-1 and N+1 Basis quartiles.

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9 (4k+1) Values

Here are the formulas and number line representations for N=9 (4k+1) using the N-1 Basis:

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Here are the formulas and number line representations for N=9 (4k+1) using the N Basis:

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Here are the formulas and number line representations for N=9 (4k+1) using the N+1 Basis:

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Same relative positions of the N-1, N, and N+1 Basis quartiles.

10 (4k+2) Values

It’s probably getting tedious, but here are the formulas and number line representations for N=10 (4k+2) using the N-1 Basis:

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Here are the formulas and number line representations for N=10 (4k+2) using the N Basis:

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Here are the formulas and number line representations for N=10(4k+2) using the N+1 Basis:

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Same pattern as before, with the N-1 quartiles closest to the median and the N+1 quartiles furthest.

11 (4k+3) Values

Finally, here are the formulas and number line representations for N=11 (4k+3) using the N-1 Basis:

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Here are the formulas and number line representations for N=11 (4k+3) using the N Basis:

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Here are the formulas and number line representations for N=11 (4k+3) using the N+1 Basis:

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No deviation from the N-1, N, N+1 trends.

Comparison of Values from All Hinge and Quartile Methods

This topic is covered in the companion page Comparison.

Quartiles in the Peltier Tech Chart Utility

This topic is covered in the companion page Quartiles in the Peltier Tech Chart Utility.

Peltier Tech Chart Utility

Hinge Techniques for Determining Quartiles

This is the second of a five part series.

Quartiles for Box Plots

This topic is covered in the companion page Quartiles for Box Plots.

Hinge Techniques for Determining Quartiles

Hinges represent an easy definition for quartiles of a data set. Arrange the values in the shape of a “W”, with equal length legs.

Arrangement of 9-value data set for definition of hinges

The central value of the data set is the value at the peak in the middle of the W, in this case, 5.

The values at the bottoms of the W, 3 and 7, are called Hinges, and they serve as quartiles in this simple definition.

Median and Hinges defined for 9-value data set

A data set makes a neat W if the number of points N can be defined by

N = 4k + 5

where k is a positive integer. For k=1, N=9, as in the example above. For k=2, N=13. A data set of 13 values is shown below.

Median and Hinges defined for 13-value data set

Alternative Hinge Definitions

To make it easier to explain and comprehend the discussions that follow, the data sets will be laid out along a number line such as this:

Number line

The gray numbers under the number line correspond to locations along the length of the set of values (as a continuous variable), while numbers above the number line correspond to the index of a particular value of the data set. A fractional number above the number line indicates that the resulting value is interpolated between the adjacent values.

Using a W-shaped data layout works for some sizes of data sets, but how can we define hinges for any arbitrarily sized data set? Such a set is shown below; it has 9 values, which we solved above, but we will use it to illustrate the general case.

9 values aligned along a number line

We can define the Median as the middle value of the data set, as always. We can define the lower hinge as the median of the bottom half of the data, and the upper hings as the median of the top half of the data. Sounds easy, right?

Inclusionary Hinge Definition (“Tukey”)

When John Tukey was laying out his first box plots, he decided that the central data point (the median) of an odd-numberd data set should be included with both lower and upper halves of the data when determining the medians of these halves, that is, when determining the hinges.

As in the “W” layout example above, the median of this 9-value example is 5, while the hinges are at 3 and 7.

Inclusive definition of upper and lower medians as hinges

Tukey’s name is generally associated with this definition of hinges.

Exclusionary Hinge Definition (“Moore and McCabe” or “M&M”)

Some statisticians didn’t like the idea of the median being counted twice by being part of both the bottom and top halves of the data. Besides, it already has an important role as Median of the data set.

These people thought that the hinges should be defined as the medians of the upper and lower halves of the data set, excluding the median. In the 9-value example, the Median is unchanged at 5, but now the hinges are 2.5 and 7.5.

Exclusive definition of upper and lower medians as hinges

This exclusionary approach to hinges is often referred to by the names of two of its proposers, Moore and McCabe, or M&M if you like candy.

If the data set has an even number of values, of course, there is no distinction between the inclusionary and exclusionary definitions, as there is no central median value to include or exclude.

Empirical Distribution Function (“CDF”)

For data sets with an even number of observations, the inclusionary (Tukey) hinges and the exclusionary (M&M) hinges are the same. For odd numbers of values, the inclusionary hinges are always closer to the median than the exclusionary hinges.

A third approach is a compromise between the Tukey and Moore-McCabe approaches. Called the Empirical Distribution Function or the Cumulative Distribution Function, or referred to by its initials CDF, it says in the case of an odd number of values in the data set, include the central median if including it will result in odd-numbered halves, and exclude the central median if excluding it will result in odd-numbered halves. This compromise results in actual values from the data set (as opposed to averages of two adjacent values) being used as hinges most of the time.

The CDF technique is the default quartile method used by the statistics package SAS, where it’s called “Empirical Distribution Function with Rounding”.

All Possible Cases of N

We can’t always lay the data out in a “W” shape. Since we’re dealing with splitting a data set into four parts, we only need to consider four cases: when the number of values N is evenly divisible by 4, or when the remainder of this division is 1, 2, or 3. These can be written as

N = 4k
N = 4k + 1
N = 4k + 2
N = 4k + 3

We will look analyze 8 (4k), 9 (4k+1), 10 (4k+2), and 11 (4k+3) values. Nine values here is a repeat of the Tukey and M&M illustration above.

8 (4k) Values

Here is our number line with a set of 8 (4k) observations.

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The median is the average of the two values closest to the center, that is, 4.5.

The lower hinge is the average of the two central values in the bottom half of the data, 2.5. The upper hinge is the average of the two central values in the top half of the data, 6.5.

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No need to hurt our brains deciding whether to include or exclude the global median from determination of the hinges. The hinges are the same for all three methods for N=4k.

9 (4k+1) Values

The 9 (4k+1) observation data set leads to two results, depending on whether the central median value is included or excluded from determination of the hinges. In both cases, the median is the central value, or 5.

In the inclusionary (Tukey) approach, the hinges are the midpoints of the data halves, or 3 and 7.

Inclusive definition of upper and lower medians as hinges

In the exclusionary (M&M) approach, the hinges are the averages of the two central values of each half of the data set, 2.5 and 7.5.

Exclusive definition of upper and lower medians as hinges

The CDF and Tukey hinges are in agreement for N=4k+1. The M&M values are slightly further from the median.

10 (4k+2) Values

In the 10 (4k+2) observation sample, the median is the average of the two central values, or 5.5.

The lower and upper hinges are the central values of the bottom and top halves of the data set, 3 and 8. No worries about the global median value here.

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The three hinge definitions all agree on the hinge values for N=4k+2 values.

11 (4k+3) Values

The 11 (4k+3) observation data set leads to two results, as in the 9 (4k+1) obervation data set. In both cases, the median is the central value, or 6.

In the inclusionary determination, the hinges are the averages of the two central values of each half of the data, 3.5 and 8.5.

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In the exclusionary (M&M) approach, the hinges are the central values of the two halves of the data set, 3 and 9.

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The CDF and M&M hinges are the same for N=4k+3, with the Tukey hinges lying slightly closer to the median.

Interpolation Methods of Determining Quartiles

This topic is covered in the companion page Quartiles.

Comparison of Values from All Hinge and Quartile Methods

This topic is covered in the companion page Comparison.

Quartiles in the Peltier Tech Chart Utility

This topic is covered in the companion page Quartiles in the Peltier Tech Chart Utility.

Peltier Tech Chart Utility

Quartiles for Box Plots

Box Plot showing variation in quartile definitions (N=9)

Any student of statistics, in fact, any middle school student, has constructed a box plot. A simple box plot (or box-and-whisker chart), like the one above, needs five parameters for each category. These are the minimum and maximum values for that category, the median value (middle value), and the first and third quartiles. In fact, the median can also be called the second quartile. The box plot above is not a meaningless sample of the chart type: it shows the variation in quartiles determined by several different methods. These will be described in excruciating detail in this tutorial.

A useful, if vague, definition of quartile is “one of three values that approximately divide a sorted data set into four parts of equal size”. This division is easy and exact, if the number of values in the set is evenly divisible by four. But in the majorityof cases, it is less certain.

Many techniques have been put forth for determining quartiles, and mostly they resolve into the handful of methods shown above, which are used by software packages. The techniques give similar, though not exactly the same, results. In this document I will describe these definitions of quartiles in hopes of shedding some light on this topic, which is more widely used than understood.

I am not a statistician, but I’ve had to understand quartiles for my Box Plot Utility. Many of my users wonder about how quartiles are calculated, so I’ve decided to document my understanding. If you have further questions, or if you find any mistakes, please let me know in the comments.

Median

The median is the central value in a sorted data set. If the values are listed from left to right in order of increasing value, there are as many values to the left of the median as to the right.

Determining the median is easy. If there is an odd number of values, the median is the value in the middle. For example, in this set of nine values, the median is the fifth value (in this case, 5), with four values below it and four above.

Median of 9 (odd) values

If there is an even number of values, the median does not correspond to a value in the data set. Instead the median is the average of the largest value in the lower half and the smallest value in the higher half. In this set of eight values, the median separates the bottom four from the top four, so we define it as the average of the fourth and fifth values, in this case, 4.5.

Median of 8 (even) values

For a small number of simple data sets, the definition of quartiles is as easy, but usually it’s more involved. Even when it’s easy, the statistical treatments make it seem harder than it is.

Hinge Techniques for Determining Quartiles

This topic is covered in the companion page Hinges.

Interpolation Methods of Determining Quartiles

This topic is covered in the companion page Quartiles.

Comparison of Values from All Hinge and Quartile Methods

This topic is covered in the companion page Comparison.

Quartiles in the Peltier Tech Chart Utility

This topic is covered in the companion page Quartiles in the Peltier Tech Chart Utility.

References

I found innumerable sources for this information about quartiles. Most were either very basic, or not useful at all. The following three are the most useful links I found.

Quartiles in Elementary Statistics
Eric Langford, California State University, Chico
Journal of Statistics Education Volume 14, Number 3 (2006).

This paper had an extensive and highly mathematical discussion of the methods described here, and several others.

Quartiles: How to calculate them?
David Journet, iTSS Wallingford

This short paper provided a summary of the SAS, Minitab, and Excel methods, supporting the information in the first reference.

Calculating Quartiles: Why Computer-Generated Results Don’t Always Agree
Delmar E. Searles, Asbury University

This article was the only place I’d ever seen a number line used to explain the difference between the N-1 and N+1 approaches to percentile definitions. I found this description almost intuitive, and decided to adopt it for all of my descriptions here. We are, after all, visual creatures, and most of us are predominantly visual learners.

Peltier Tech Chart Utility

Peltier Tech Chart Utility

 

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