Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Regression Analysis01:11

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares the...
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
One-Way ANOVA01:18

One-Way ANOVA

One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
Two-Way ANOVA01:17

Two-Way ANOVA

The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the means for...
Scatter Plot01:15

Scatter Plot

The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Effects of feeding strategies on culture performance and product quality in NISTCHO.

NPJ systems biology and applications·2026
Same author

Efficient spline orthogonal basis for representation of density functions.

Journal of applied statistics·2026
Same author

Orthonormal pairwise logratio selection (OPALS) algorithm for compositional data analysis in high dimensions.

Bioinformatics advances·2025
Same author

Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data.

Nature communications·2025
Same author

Robust multivariate regression controlling false discoveries for microbiome data.

Bioinformatics (Oxford, England)·2025
Same author

The goldilocks days represent optimal time-use to prevent obesity, low physical performance, risk and fear of falling in older adults.

Scientific reports·2025
Same journal

Retraction notice to "Effect of ferrous-activated calcium peroxide oxidation on forward osmosis treatment of algae-laden water: Membrane fouling mitigation and mechanism" [Sci. Total Environ. 858 (2023) 160100].

The Science of the total environment·2026
Same journal

Retraction notice to "Algorithm developed for dynamic quantification of coal consumption for and emission from rural winter heating" [Sci. Total Environ. 737 (2020) 139762].

The Science of the total environment·2026
Same journal

Retraction notice to "Spatial and temporal distribution of urban heat islands" [Sci. Total Environ. 605-606 (2017) 946-956].

The Science of the total environment·2026
Same journal

Retraction notice to "Scenario analysis on optimal farmed-fish-species composition in China: A tentative theoretical methodology to benefit wild-fishery stock, water conservation, economic and protein outputs under the context of climate change" [Sci. Total Environ. 806 (2022) 150600].

The Science of the total environment·2026
Same journal

Retraction notice to "Study on the effect of SDBS and SDS on deep coal seam water injection" [Sci. Total Environ. 856 (2023) 158930].

The Science of the total environment·2026
Same journal

Retraction notice to "Social, economic and environmental vulnerability: The case of wheat farmers in Northeast Iran" [Sci. Total Environ. 816 (2022) 151519].

The Science of the total environment·2026
See all related articles

Related Experiment Video

Updated: Jun 12, 2026

Visualization of Productivity Zones Based on Nitrogen Mass Balance Model in Narragansett Bay, Rhode Island
05:04

Visualization of Productivity Zones Based on Nitrogen Mass Balance Model in Narragansett Bay, Rhode Island

Published on: July 14, 2023

The bivariate statistical analysis of environmental (compositional) data.

Peter Filzmoser1, Karel Hron, Clemens Reimann

  • 1Vienna University of Technology, Department of Statistics and Probability Theory, A-1040 Vienna, Austria. P.Filzmoser@tuwien.ac.at

The Science of the Total Environment
|June 15, 2010
PubMed
Summary
This summary is machine-generated.

Environmental sciences often use compositional data, where ratios matter more than values. This study explores statistical analysis challenges with this data type and introduces the ilr transformation for better interpretation.

More Related Videos

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
13:02

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
05:45

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Published on: January 7, 2019

Related Experiment Videos

Last Updated: Jun 12, 2026

Visualization of Productivity Zones Based on Nitrogen Mass Balance Model in Narragansett Bay, Rhode Island
05:04

Visualization of Productivity Zones Based on Nitrogen Mass Balance Model in Narragansett Bay, Rhode Island

Published on: July 14, 2023

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
13:02

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
05:45

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Published on: January 7, 2019

Area of Science:

  • Environmental sciences
  • Geochemistry
  • Statistical analysis

Background:

  • Environmental science data is often compositional, meaning relative proportions are key.
  • Traditional statistical methods assume Euclidean geometry, which is unsuitable for compositional data.
  • This mismatch leads to misleading results in bivariate statistical analysis.

Purpose of the Study:

  • To address the challenges of analyzing compositional data in environmental sciences.
  • To introduce and evaluate the isometric log-ratio (ilr) transformation for bivariate analysis.
  • To provide guidance on interpreting relationships within compositional datasets.

Main Methods:

  • Discussing the implications of data closure on statistical analysis.
  • Applying the ilr transformation to pairs of compositional variables.
  • Comparing the ilr-derived stability measure with traditional correlation coefficients.

Main Results:

  • Traditional correlation coefficients can be misleading for compositional data.
  • The ilr transformation provides a valid method for visualizing and quantifying relationships.
  • The stability measure derived from ilr offers a robust alternative to standard correlation.

Conclusions:

  • Compositional data requires specialized statistical approaches beyond standard Euclidean methods.
  • The ilr transformation is a powerful tool for analyzing bivariate relationships in compositional data.
  • Accurate interpretation of environmental compositional data necessitates understanding its unique geometric properties.