Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
On testing the equality between interquartile ranges
by
Greco, Luca
, Luta, George
, Wilcox, Rand
in
Contingency tables
/ Equality
/ Numerical analysis
/ Quantiles
/ Random variables
/ Statistical analysis
/ Statistics
2024
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
On testing the equality between interquartile ranges
by
Greco, Luca
, Luta, George
, Wilcox, Rand
in
Contingency tables
/ Equality
/ Numerical analysis
/ Quantiles
/ Random variables
/ Statistical analysis
/ Statistics
2024
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
On testing the equality between interquartile ranges
2024
Request Book From Autostore
and Choose the Collection Method
Overview
The interquartile range is a statistical measure well suited to describe the variability of the data at hand, both at the population level and for sample data. The interquartile range is particularly useful when the distribution of the data is asymmetric or irregularly shaped. Here, the use of the interquartile range is investigated when the main aim is to compare the variability of two distributions using two independent random samples, without the need to make any distributional assumptions. Several techniques are compared through numerical studies and real data examples, with a particular attention given to the use of sample quantiles based on the Harrel-Davis estimator or the quantile regression.
Publisher
Springer Nature B.V
Subject
This website uses cookies to ensure you get the best experience on our website.