Maple Professional
Maple Academic
Maple Student Edition
Maple Personal Edition
Maple Player
Maple Player for iPad
MapleSim Professional
MapleSim Academic
Maple T.A. - Testing & Assessment
Maple T.A. MAA Placement Test Suite
Möbius - Online Courseware
Machine Design / Industrial Automation
Aerospace
Vehicle Engineering
Robotics
Power Industries
System Simulation and Analysis
Model development for HIL
Plant Modeling for Control Design
Robotics/Motion Control/Mechatronics
Other Application Areas
Mathematics Education
Engineering Education
High Schools & Two-Year Colleges
Testing & Assessment
Students
Financial Modeling
Operations Research
High Performance Computing
Physics
Live Webinars
Recorded Webinars
Upcoming Events
MaplePrimes
Maplesoft Blog
Maplesoft Membership
Maple Ambassador Program
MapleCloud
Technical Whitepapers
E-Mail Newsletters
Maple Books
Math Matters
Application Center
MapleSim Model Gallery
User Case Studies
Exploring Engineering Fundamentals
Teaching Concepts with Maple
Maplesoft Welcome Center
Teacher Resource Center
Student Help Center
Statistics[InterquartileRange] - compute the interquartile range
Calling Sequence
InterquartileRange(A, ds_options)
InterquartileRange(M, ds_options)
InterquartileRange(X, rv_options)
Parameters
A
-
Array; data sample
M
Matrix data set
X
algebraic; random variable or distribution
ds_options
(optional) equation(s) of the form option=value where option is one of ignore, or weights; specify options for computing the interquartile range of a data set
rv_options
(optional) equation of the form numeric=value; specifies options for computing the interquartile range of a random variable
Description
The InterquartileRange function computes the interquartile range of the specified random variable or data set.
The first parameter can be a data set (represented as an Array or a Matrix data set), a distribution (see Statistics[Distribution]), a random variable, or an algebraic expression involving random variables (see Statistics[RandomVariable]).
Computation
By default, all computations involving random variables are performed symbolically (see option numeric below).
All computations involving data are performed in floating-point; therefore, all data provided must have type realcons and all returned solutions are floating-point, even if the problem is specified with exact values.
For more information about computation in the Statistics package, see the Statistics[Computation] help page.
Data Set Options
The ds_options argument can contain one or more of the options shown below. More information for some options is available in the Statistics[DescriptiveStatistics] help page.
ignore=truefalse -- This option controls how missing data is handled by the InterquartileRange command. Missing items are represented by undefined or Float(undefined). So, if ignore=false and A contains missing data, the InterquartileRange command will return undefined. If ignore=true all missing items in A will be ignored. The default value is false.
weights=Vector -- Data weights. The number of elements in the weights array must be equal to the number of elements in the original data sample. By default all elements in A are assigned weight .
Random Variable Options
The rv_options argument can contain one or more of the options shown below. More information for some options is available in the Statistics[RandomVariables] help page.
numeric=truefalse -- By default, the interquartile range is computed symbolically. To compute the interquartile range numerically, specify the numeric or numeric = true option.
Compatibility
The M parameter was introduced in Maple 16.
For more information on Maple 16 changes, see Updates in Maple 16.
Examples
Compute the average absolute range from the interquartile of the Rayleigh distribution with parameter 3.
Generate a random sample of size 100000 drawn from the above distribution and compute the sample interquartile range.
Compute the standard error of the interquartile range for the normal distribution with parameters 5 and 2.
Compute the interquartile range of a weighted data set.
Consider the following Matrix data set.
We compute the interquartile range of each of the columns.
See Also
Statistics, Statistics[Computation], Statistics[DescriptiveStatistics], Statistics[Distributions], Statistics[ExpectedValue], Statistics[RandomVariables], Statistics[StandardError]
References
Stuart, Alan, and Ord, Keith. Kendall's Advanced Theory of Statistics. 6th ed. London: Edward Arnold, 1998. Vol. 1: Distribution Theory.
Download Help Document
