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

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

9.6K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
9.6K
Outliers and Influential Points01:08

Outliers and Influential Points

6.5K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
6.5K
Survival Tree01:19

Survival Tree

443
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
443
Regression Analysis01:11

Regression Analysis

8.6K
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:
8.6K
Multiple Regression01:25

Multiple Regression

4.1K
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...
4.1K
Regression Toward the Mean01:52

Regression Toward the Mean

7.2K
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...
7.2K

You might also read

Related Articles

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

Sort by
Same author

Variable selection for clinical prediction models in low-dimensional data - a simulation study comparing traditional regression and machine learning methods.

BMC medical research methodology·2026
Same author

When to Adjust for Multiple Testing: A Unifying Guiding Principle.

Biometrical journal. Biometrische Zeitschrift·2026
Same author

Donor bone marrow together with recipient regulatory T cells induces chimerism without irradiation in kidney transplantation.

Science translational medicine·2026
Same author

Using routinely collected data for research purposes: challenges and mitigation strategies.

BMJ (Clinical research ed.)·2026
Same author

First attempt success rate of intraosseous access in preterm infants and neonates: a systematic review.

Resuscitation plus·2026
Same author

Plasma aldosterone is low in patients hospitalized with COVID-19 and not associated with changes in serum potassium levels: <i>post hoc</i> observational analyses of clinical trial data.

Frontiers in endocrinology·2025

Related Experiment Video

Updated: Feb 21, 2026

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

10.9K

Separation in Logistic Regression: Causes, Consequences, and Control.

Mohammad Ali Mansournia1,1, Angelika Geroldinger2,2, Sander Greenland3,4,3,4

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

American Journal of Epidemiology
|October 12, 2017
PubMed
Summary
This summary is machine-generated.

Separation in logistic regression occurs when covariates perfectly predict outcomes, leading to unreliable estimates. Penalized-likelihood methods effectively address this bias, improving model accuracy and enabling Bayesian interpretations.

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

774
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.8K

Related Experiment Videos

Last Updated: Feb 21, 2026

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

10.9K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

774
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.8K

Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Separation is a problem in logistic regression where covariates perfectly predict discrete outcomes.
  • It often occurs with rare outcomes, sparse data, or highly correlated covariates, leading to infinite coefficient estimates.
  • Software limitations can cause separation to go unnoticed or be mishandled.

Purpose of the Study:

  • To discuss the causes and consequences of separation in logistic regression.
  • To describe how common statistical software packages handle separation.
  • To present penalized-likelihood methods for removing separation and improving model accuracy.

Main Methods:

  • Discussed causes of separation in logistic regression models.
  • Reviewed software handling of separation issues.
  • Described penalized-likelihood techniques as solutions for separation and sparse-data problems.
  • Illustrated methods with a case-control study on contraceptive use and urinary tract infection.

Main Results:

  • Separation leads to infinite coefficient estimates and can be unnoticed in practice.
  • Penalized-likelihood methods effectively remove separation.
  • These methods improve model accuracy and avoid software issues.
  • Penalized methods can be interpreted as Bayesian analyses with weakly informative priors.

Conclusions:

  • Penalized-likelihood methods offer a robust solution to separation in logistic regression.
  • These techniques enhance statistical model reliability and interpretability.
  • Implementation is feasible, even with standard software packages, offering advantages over default handling.