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 Toward the Mean01:52

Regression Toward the Mean

6.9K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.9K
Acid and Bases: Ka, pKa, and Relative Strengths02:35

Acid and Bases: Ka, pKa, and Relative Strengths

33.0K
This lesson delves into a critical aspect of the relative strengths of acids and bases. The strength of an acid is evaluated by the acid dissociation into its conjugate base and a hydronium ion in water. The complete dissociation of a strong acid is confirmed with a very high concentration of hydronium ions. As a result, an incomplete dissociation process affirms a weak acid. Therefore, the equilibrium is in the forward direction for strong acids and backward for weak acids in these reactions.
33.0K
Multiple Regression01:25

Multiple Regression

3.8K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.8K
Relative Strengths of Conjugate Acid-Base Pairs02:29

Relative Strengths of Conjugate Acid-Base Pairs

51.7K
Brønsted-Lowry acid-base chemistry is the transfer of protons; thus, logic suggests a relation between the relative strengths of conjugate acid-base pairs. The strength of an acid or base is quantified in its ionization constant, Ka or Kb, which represents the extent of the acid or base ionization reaction. For the conjugate acid-base pair HA / A−, the ionization equilibrium equations and ionization constant expressions are
51.7K
Correlation and Regression00:53

Correlation and Regression

3.2K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.2K
Regression Analysis01:11

Regression Analysis

8.1K
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:
8.1K

You might also read

Related Articles

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

Sort by
Same author

Identifying patients at elevated risk of stroke after transcarotid artery revascularization.

Journal of vascular surgery·2026
Same author

Measuring How Palliative Care is Delivered: Using Provider Sequences as a New Quality Signal.

Journal of pain and symptom management·2026
Same author

Gaming the System: Evaluating Spillover in a Video Game Intervention for Advance Care Planning using Physician Social Networks.

Health services & outcomes research methodology·2026
Same author

Instrumental variable estimation of a hazard ratio for treatment with a waiting time without specifying its dependence on unmeasured confounders: application to a procedural registry.

Lifetime data analysis·2026
Same author

Network threats to causal inference: Variations in network position by participation in randomized controlled trials.

Social networks·2026
Same author

Itching to know: the Microbiome, Allergic Disease, and Antimicrobial Chemicals.

Current environmental health reports·2026

Related Experiment Video

Updated: Jan 27, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.3K

Conditional Regression Based on a Multivariate Zero-Inflated Logistic-Normal Model for Microbiome Relative Abundance

Zhigang Li1,2,3,4, Katherine Lee5, Margaret R Karagas2,3

  • 1Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, 1 Medical Center Drive, Lebanon, NK 03756, USA.

Statistics in Biosciences
|March 30, 2019
PubMed
Summary

A new statistical model, the multivariate two-part zero-inflated logistic normal (MZILN) model, effectively analyzes human microbiome data. This approach addresses challenges like zero values and high dimensionality, improving disease risk association studies.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.8K
Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis
10:50

Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

Published on: November 2, 2018

8.4K

Related Experiment Videos

Last Updated: Jan 27, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.3K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.8K
Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis
10:50

Detection and Quantification of Plasmodium falciparum in Aqueous Red Blood Cells by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis

Published on: November 2, 2018

8.4K

Area of Science:

  • Microbiome research
  • Statistical modeling
  • Bioinformatics

Background:

  • The human microbiome is crucial for health and disease, but analyzing its complex data is challenging.
  • Existing methods struggle with excessive zeros, high dimensionality, and compositional data in microbiome studies.
  • Understanding microbiome-disease links requires advanced analytical tools.

Purpose of the Study:

  • To introduce a novel statistical model for analyzing human microbiome data.
  • To address limitations of existing methods in handling complex microbiome data structures.
  • To investigate associations between disease risk factors and microbial composition.

Main Methods:

  • Development of a multivariate two-part zero-inflated logistic normal (MZILN) model.
  • Utilizing an estimating equations approach for parameter estimation.
  • Incorporating regularization techniques for high-dimensional data analysis.

Main Results:

  • The MZILN model effectively handles excessive zeros and compositional data.
  • The proposed method demonstrates superior performance compared to existing approaches in simulations.
  • The model successfully analyzes real microbiome data, revealing disease risk factor associations.

Conclusions:

  • The MZILN model offers a robust framework for microbiome data analysis.
  • This approach enhances our ability to study the interplay between the microbiome and disease risk.
  • The developed methodology advances the field of microbiome-based health research.