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

Correlations02:20

Correlations

33.4K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
33.4K
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

3.7K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
3.7K
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

969
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
969
Correlation and Regression00:53

Correlation and Regression

1.4K
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...
1.4K
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.2K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.2K
Regression Analysis01:11

Regression Analysis

5.9K
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:
5.9K

You might also read

Related Articles

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

Sort by
Same author

Modeling injury severity in embankment-related roadway departure crashes.

Scientific reports·2026
Same author

Behavioral and temporal dynamics of child pedestrian crash injury patterns: evidence from random parameter modeling.

Scientific reports·2026
Same author

Decoding pedestrian severity at crosswalks using hybrid clustering and random parameter models.

Scientific reports·2026
Same author

Temporal dynamics in barrier-involved crashes: Determining shifts in driving behavior and injury risk across multi-year periods.

Scientific reports·2026
Same author

Hybrid dimension reduction and logit models for glare-induced crash severity.

Scientific reports·2026
Same author

Impact assessment of contributing factors in direct-impact work zone crashes using heterogeneity and constrained-based discrete choice models.

Accident; analysis and prevention·2026
Same journal

School zone speed compliance in the ACT, Australia: Risks, findings and recommendations for improved safety.

Journal of safety research·2026
Same journal

A bibliometric performance and network analysis of red-light camera impact on signalized intersection safety.

Journal of safety research·2026
Same journal

Effects of driver direct visibility in passenger vehicles on the risk of turning crashes with pedestrians.

Journal of safety research·2026
Same journal

The importance of employee-engaged safety audits to reduce LTI and safety-related costs.

Journal of safety research·2026
Same journal

Improving crash data quality by identifying misclassified alcohol-involved crashes using NLP on narrative data.

Journal of safety research·2026
Same journal

The influence of employee health and safety policies on the value of the organization.

Journal of safety research·2026
See all related articles

Related Experiment Video

Updated: Aug 8, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.5K

Understanding the drowsy driving crash patterns from correspondence regression analysis.

M Ashifur Rahman1, Subasish Das2, Xiaoduan Sun1

  • 1University of Louisiana at Lafayette, 104 E University Circle, Lafayette, LA 70503, USA.

Journal of Safety Research
|March 3, 2023
PubMed
Summary
This summary is machine-generated.

Drowsy driving crashes in Louisiana are a serious safety issue. Key patterns and driver attributes are linked to severe injuries, informing targeted prevention strategies.

Keywords:
Correspondence regressionDriver behaviorDrowsy drivingFatigueSleepy drivers

More Related Videos

Driving Under the Influence: How Music Listening Affects Driving Behaviors
07:25

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Published on: March 27, 2019

12.5K
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.5K

Related Experiment Videos

Last Updated: Aug 8, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.5K
Driving Under the Influence: How Music Listening Affects Driving Behaviors
07:25

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Published on: March 27, 2019

12.5K
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.5K

Area of Science:

  • Transportation Safety
  • Accident Analysis
  • Public Health

Background:

  • Drowsy driving crashes are a significant transportation safety concern.
  • In Louisiana, 14% of drowsy driving crashes from 2015-2019 resulted in injuries.
  • National agencies highlight the need to address drowsy driving behaviors and crash severity.

Purpose of the Study:

  • To explore key reportable attributes of drowsy driving behaviors.
  • To investigate the association between these attributes and crash severity.
  • To identify patterns in drowsy driving-related crashes based on injury levels.

Main Methods:

  • Utilized 5-year (2015-2019) crash data from Louisiana.
  • Employed correspondence regression analysis.
  • Identified collective associations of attributes and interpretable patterns based on injury levels.

Main Results:

  • Identified crash patterns: afternoon fatigue, young drivers on low-speed roads, male drivers in dark/rainy conditions, pickup trucks in industrial areas, late-night crashes, and heavy trucks on curves.
  • Attributes like rural areas, multiple passengers, and older drivers (over 65) strongly associated with fatal/severe injury crashes.

Conclusions:

  • Findings provide insights into specific drowsy driving crash scenarios and contributing factors.
  • Results can inform targeted mitigation strategies for policymakers and researchers.
  • Understanding these patterns is crucial for developing effective drowsy driving prevention measures.