English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Data analysis is the core heart of quantitative research. An important part of data analysis is outlier detection. Several outlier detection techniques have been developed in the past that include a popular technique named as Tukey’s boxplot. Tukey’s boxplot uses inter quartile range to detect outliers on both sides of median. This method works well in symmetric distributions while it constructs misleading fence in skewed distributions. Several amendments have been made in Tukey’s method to detect outliers in skewed distributions. Latest amendment is suggested by Hubert and Vandervieren who incorporated the exponent of medcouple multiplied by different constants conditioned on the direction of skewness. This paper provides a novel approach to fix the problem by splitting the data in two halves from the median and constructing fence separately on both sides of the median. Splitting process enables detection process efficiently, making it robust. Mathematical calculation of probability of Type-I error and interval width proved that this method has superiority on Tukey and Hubert and Vandervieren method.

Outliers Detection in Skewed Distributions: Split Sample Skewness Based Boxplot