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

8.4K
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:
8.4K
Regression Toward the Mean01:52

Regression Toward the Mean

7.0K
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...
7.0K
Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

1.5K
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
1.5K
Multiple Regression01:25

Multiple Regression

4.0K
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...
4.0K
Correlation and Regression00:53

Correlation and Regression

3.4K
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...
3.4K
Nursing Interventions I: Taxonomy of Nursing Interventions01:03

Nursing Interventions I: Taxonomy of Nursing Interventions

3.7K
Nursing interventions are chosen as part of the planning process to achieve patient outcomes. Once nursing diagnoses are determined, the goals and outcomes are specified, then the nursing interventions are selected and individualized according to the patient's situation.
A nursing intervention is a treatment or action based on scientific concepts and knowledge from the nursing, behavioral, and physical sciences. Identifying and prioritizing nursing interventions based on the desired outcome...
3.7K

You might also read

Related Articles

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

Sort by
Same author

[Epidemiological and clinical characteristics of pertussis in children at a tertiary pediatric hospital in Shanghai City in 2024].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]·2026
Same author

[A retrospective study of the impact of interpregnancy interval on preterm birth].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2026
Same author

[Analysis of the comorbidity prevalence and related factors of elevated blood pressure and overweight and obesity in children aged 7-9 years in Zhejiang Province from 2018 to 2024].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]·2026
Same author

[Advances in artificial intelligence-based nasal endoscopy in the diagnosis and treatment of rhinologic diseases].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery·2026
Same author

[Moderate-intensity statin plus ezetimibe: time to rethink it as an optimal initial lipid-lowering strategy].

Zhonghua xin xue guan bing za zhi·2026
Same author

[Application of a novel intestinal diversion stent in the treatment of mid-low rectal anastomotic leakage].

Zhonghua wei chang wai ke za zhi = Chinese journal of gastrointestinal surgery·2026

Related Experiment Video

Updated: Jan 28, 2026

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training
09:57

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Published on: January 18, 2021

4.6K

Planning area-specific prevention and intervention programs for HIV using spatial regression analysis.

S Das1, J J Li2, A Allston1

  • 1Strategic Information Division, HIV/AIDS, Hepatitis, STD, and TB Administration (HAHSTA), District of Columbia Department of Health, 899 North Capitol St. NE / Fourth Floor, Washington, DC 20002, USA.

Public Health
|March 1, 2019
PubMed
Summary
This summary is machine-generated.

Spatial regression identified key factors associated with new HIV diagnoses in Washington D.C., guiding targeted prevention and resource allocation for reducing infections.

Keywords:
District of ColumbiaGeographically weighted regressionHIVSTIsSpatial variation

More Related Videos

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
11:10

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

Published on: December 27, 2010

12.7K
Treatment of Facial Deformities using 3D Planning and Printing of Patient-Specific Implants
07:11

Treatment of Facial Deformities using 3D Planning and Printing of Patient-Specific Implants

Published on: May 23, 2020

7.9K

Related Experiment Videos

Last Updated: Jan 28, 2026

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training
09:57

Development and Evaluation of 3D-Printed Cardiovascular Phantoms for Interventional Planning and Training

Published on: January 18, 2021

4.6K
Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
11:10

Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3

Published on: December 27, 2010

12.7K
Treatment of Facial Deformities using 3D Planning and Printing of Patient-Specific Implants
07:11

Treatment of Facial Deformities using 3D Planning and Printing of Patient-Specific Implants

Published on: May 23, 2020

7.9K

Area of Science:

  • Public Health
  • Epidemiology
  • Spatial Analysis

Background:

  • Understanding the spatial distribution of new Human Immunodeficiency Virus (HIV) diagnoses is crucial for effective public health interventions.
  • Identifying socio-economic and demographic factors associated with HIV transmission can inform targeted prevention strategies.
  • Previous analyses may not have fully captured the localized variations in HIV rates and their drivers.

Purpose of the Study:

  • To inform area-based HIV prevention intervention programs in the District of Columbia (DC).
  • To guide resource allocation for reducing new HIV infections.
  • To analyze the spatial heterogeneity of new HIV rates and their association with specific risk factors.

Main Methods:

  • Utilized spatial regression techniques, including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), to analyze HIV and sexually transmitted infection repeater (STIREP) data from DC Department of Health (2010-2016).
  • Incorporated socio-economic and demographic covariates from the American Community Survey (2016).
  • Compared global and local spatial relationships to identify significant predictors of new HIV diagnoses.

Main Results:

  • The OLS model indicated associations between age, high school dropouts (NHSD), and the Black population with new HIV diagnoses.
  • The GWR model revealed significant spatial variations in the association of STIREPs, mean age, female population percentage, NHSD, unemployment, and poverty with new HIV diagnoses.
  • GWR demonstrated a substantial improvement in model fit (R-squared increased by 27%) compared to the global OLS model, indicating localized patterns.

Conclusions:

  • The findings provide critical insights for planning targeted HIV prevention and intervention strategies in DC.
  • Results support the allocation of resources for targeted testing, pre-exposure prophylaxis (PrEP), and healthcare access.
  • The study aids in directing resources to community-based providers and public health programs like condom distribution and sex education.